diff --git a/.github/workflows/desktop.yml b/.github/workflows/desktop.yml index 50eb03ee..5a0049ad 100644 --- a/.github/workflows/desktop.yml +++ b/.github/workflows/desktop.yml @@ -183,10 +183,10 @@ jobs: run: npm run check working-directory: desktop - - name: Verify generated desktop contracts and notices + - name: Verify generated desktop inputs and notices run: | + npm run model-catalog:generate cargo fetch --manifest-path src-tauri/Cargo.toml --locked - npm run model-catalog:check npm run notices:check working-directory: desktop diff --git a/.gitignore b/.gitignore index f4b94a05..17a81dbd 100644 --- a/.gitignore +++ b/.gitignore @@ -42,6 +42,7 @@ pnpm-debug.log* /target /desktop/node_modules/ /desktop/dist/ +/desktop/generated/ /premiere/dist/ /premiere/ccx/ /premiere/coverage/ diff --git a/AGENTS.md b/AGENTS.md index 5fb1c801..c423f453 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -12,7 +12,13 @@ share the same application contracts. Read - Keep capability-specific models, schemas, dependencies, indexing, and search logic under `src/vidxp/capabilities/`. - Do not commit generated environments, model weights, media, indexes, build - outputs, or local data. + outputs, local databases, or other bulky or machine-specific data. +- You may commit lightweight, sanitized benchmark result artifacts when they + support the change or a documented research claim and remain useful from a + different checkout. Reference each retained artifact from the owning + benchmark documentation and include only the configuration, provenance, + metrics, and traces needed to audit it. Contributors may propose these + artifacts; maintainers decide whether they remain before merge. - Follow the Conventional Commit and release-note rules in the contributing guide. State explicitly when a change is internal-only. diff --git a/INSTALLATION_GUIDE.md b/INSTALLATION_GUIDE.md index a35ba255..f627210d 100644 --- a/INSTALLATION_GUIDE.md +++ b/INSTALLATION_GUIDE.md @@ -245,6 +245,22 @@ vidxp search speech "the bread just came out of the oven" Add `--media-id ` to a search command to restrict results to one video. Without it, VidXP searches all indexed videos in the active repository. +Sound indexing defaults to one ten-second inference section per batch, two +seconds of overlap, and ten-second search results. Override a value only when +running a declared experiment, for example: + +```bash +vidxp index create --modality sound \ + --option sound.inference_overlap_seconds=5 +``` + +The corresponding option names are `batch_size`, +`inference_window_seconds`, `inference_overlap_seconds`, and +`evidence_window_seconds`. The overlap must be shorter than the inference +window. The inference window bounds one model call, not the media duration; +long videos are processed as successive sections. These settings change the +index profile and require the affected media to be indexed again. + ### Start an installed interface | Interface | Command | @@ -316,26 +332,33 @@ and sharing behavior. ## Optional local grounded answers VidXP search does not require a language model. To let CLI, HTTP, or MCP -queries plan searches and draft grounded answers locally, enable **Local -grounded answers** in VidXP Desktop setup. Desktop checks for a compatible -loopback Ollama service and then for an existing Ollama executable. When neither -is available on Windows x86-64 or macOS Apple Silicon, Desktop asks before -downloading a pinned, checksum-verified headless runtime into VidXP's private -data. It does not install the Ollama desktop app. Linux setup links to Ollama's -official installation instructions instead of running a privileged script. +queries plan searches and draft grounded answers locally, run: + +```bash +vidxp local-answers prepare +vidxp local-answers status +``` + +The recommended `local-worker` installation already includes the required +client. Custom package installations must include the `slm` extra. + +Preparation reuses a healthy self-hosted Ollama service or an existing Ollama +executable. When neither is available on Windows x86-64 or macOS Apple Silicon, +VidXP discloses the download sizes and asks before installing its pinned, +checksum-verified headless runtime. It does not install the Ollama desktop app. +Linux requires an existing Ollama installation. This optional feature follows Ollama's platform floor: Windows 10 22H2 or newer, or macOS 14 or newer. VidXP Desktop itself can still run without local grounded answers on older supported systems. -The model is an additional approximately 3.4 GB download. When Desktop must -provide the headless runtime, that download is up to approximately 1.36 GiB; -reusing Ollama avoids it. Local answers have no per-run API charge or numbered -hosted-model allowance, but they use local storage, memory, compute time, and -electricity. Desktop configures the private service address for its browser, -worker, API, Premiere, and generated MCP/Codex setup; there is no URL field to -fill in. A command-line-only installation remains available for developers and -custom deployments. +The model is an additional approximately 3.4 GB download. A managed headless +runtime can add up to approximately 1.36 GiB; reusing Ollama avoids it. Local +answers have no per-run API charge or numbered hosted-model allowance, but they +use local storage, memory, compute time, and electricity. The command saves the +local endpoint and model selection, so later VidXP CLI, HTTP, and MCP processes +do not need shell exports. Desktop setup invokes the same preparation operation +inside its managed runtime and carries the settings into Desktop-owned services. The complete setup and its current evidence limitations are documented under [Enable local grounded answers](docs/local-api.md#enable-local-grounded-answers). @@ -427,9 +450,12 @@ vidxp doctor ``` When an upgrade changes a search model or index format, existing videos may need -to be indexed again. VidXP reports this instead of silently replacing a working -index. Prepare the required models, re-index the affected videos, and keep the -old repository until you have checked the replacement results. +to be indexed again. Index schema 8 replaces FineLAP sound records with +PE-A-Frame records and changes vector collections to inner-product ranking, so +repositories from schema 7 must be rebuilt. VidXP reports the incompatibility +instead of silently replacing a working index. Prepare the required models, +re-index the affected videos, and keep the old repository until you have checked +the replacement results. The current public capability names are `scene`, `action`, `sound`, `speech`, and `actor`. VidXP does not translate removed capability names. diff --git a/README.md b/README.md index a180c453..bd12fd43 100644 --- a/README.md +++ b/README.md @@ -170,6 +170,10 @@ questions, and return inspectable evidence such as boards, frames, and clips. A local client can start VidXP as a program on the same computer. A hosted client connects to a deployed VidXP server. +The goal is to give the agent useful eyes and ears without sending the whole +video through its context. VidXP narrows the library to timestamped evidence; +the agent inspects that evidence and decides what it means. + ### Codex plugin and skills VidXP is distributed as a Codex plugin through a Git marketplace hosted in diff --git a/benchmarks/codex-mcp/package.json b/benchmarks/codex-mcp/package.json index 2621e83a..a7c366ef 100644 --- a/benchmarks/codex-mcp/package.json +++ b/benchmarks/codex-mcp/package.json @@ -2,18 +2,23 @@ "name": "vidxp-codex-mcp-eval", "private": true, "version": "0.0.0", - "description": "Paired Codex evaluation with and without the local VidXP MCP server", + "description": "VidXP agent-ablation and local-agent evaluation", "engines": { "node": ">=22.22.0" }, "scripts": { "setup": "node scripts/setup.mjs", - "test:setup": "node --test scripts/setup.test.mjs", + "test:setup": "node --no-warnings --test scripts/setup.test.mjs scripts/report.test.mjs", "promptfoo": "node --env-file=.env node_modules/promptfoo/dist/src/entrypoint.js", "check": "node scripts/require-node.mjs && node --env-file-if-exists=.env node_modules/promptfoo/dist/src/entrypoint.js validate -c promptfooconfig.yaml", "preflight": "node --env-file=.env scripts/preflight.mjs", - "eval:smoke": "npm run preflight && npm run promptfoo -- eval -c promptfooconfig.yaml --filter-first-n 2 --repeat 1 --no-cache --no-share", - "eval:pilot": "npm run preflight && npm run promptfoo -- eval -c promptfooconfig.yaml --repeat 3 --no-cache --no-share", + "eval:smoke": "node scripts/require-node.mjs && node --env-file=.env --no-warnings scripts/run-eval.mjs smoke", + "eval:pilot": "node scripts/require-node.mjs && node --env-file=.env --no-warnings scripts/run-eval.mjs pilot", + "eval:vidxp": "node scripts/require-node.mjs && node --env-file=.env --no-warnings scripts/run-eval.mjs pilot vidxp-on", + "eval:slm:smoke": "node scripts/require-node.mjs && node --env-file=.env --no-warnings scripts/run-eval.mjs smoke local-slm,local-slm-planner", + "eval:slm": "node scripts/require-node.mjs && node --env-file=.env --no-warnings scripts/run-eval.mjs pilot local-slm,local-slm-planner", + "export": "node --env-file=.env --no-warnings scripts/export-eval.mjs", + "report": "node --env-file-if-exists=.env --no-warnings scripts/report.mjs", "view": "npm run promptfoo -- view" }, "devDependencies": { diff --git a/benchmarks/codex-mcp/promptfooconfig.yaml b/benchmarks/codex-mcp/promptfooconfig.yaml index d8e1e837..673ef06d 100644 --- a/benchmarks/codex-mcp/promptfooconfig.yaml +++ b/benchmarks/codex-mcp/promptfooconfig.yaml @@ -1,5 +1,8 @@ # yaml-language-server: $schema=https://promptfoo.dev/config-schema.json -description: VidXP Codex MCP-on versus MCP-off temporal evidence evaluation +description: VidXP agent ablation and local-planning temporal evidence evaluation + +extensions: + - file://scripts/reset-workspace.mjs:beforeEach prompts: - id: video-evidence-task @@ -8,16 +11,14 @@ prompts: providers: - id: openai:codex-sdk - label: codex-vidxp-mcp - config: + label: codex-vidxp + config: &vidxp_provider model: "{{ env.VIDXP_EVAL_MODEL | default('gpt-5.6-sol') }}" model_reasoning_effort: "{{ env.VIDXP_EVAL_REASONING | default('medium') }}" maxRetries: 0 - working_dir: "{{ env.VIDXP_EVAL_WORKSPACE }}" + working_dir: "{{ env.VIDXP_EVAL_VIDXP_ON_WORKSPACE }}" skip_git_repo_check: true - sandbox_mode: read-only approval_policy: never - network_access_enabled: false web_search_mode: disabled persist_threads: false enable_streaming: true @@ -27,65 +28,65 @@ providers: required: - video_id - answer - - start_seconds - - end_seconds - - modalities - - evidence + - source_job_id + - candidates properties: video_id: type: string answer: type: string - start_seconds: - type: - - number - - "null" - end_seconds: + source_job_id: type: - - number + - string - "null" - modalities: - type: array - uniqueItems: true - items: - type: string - enum: - - scene - - action - - sound - - speech - evidence: + candidates: type: array + minItems: 0 + maxItems: 3 items: type: object additionalProperties: false required: - start_seconds - end_seconds - - modality + - modalities - description + - evidence_ids properties: start_seconds: type: number end_seconds: type: number - modality: - type: string - enum: - - scene - - action - - sound - - speech + modalities: + type: array + items: + type: string + enum: + - scene + - action + - sound + - speech description: type: string + evidence_ids: + type: array + items: + type: string cli_env: - CODEX_HOME: "{{ env.VIDXP_EVAL_CODEX_HOME }}" + CODEX_HOME: "{{ env.VIDXP_EVAL_VIDXP_ON_CODEX_HOME }}" + HOME: "{{ env.VIDXP_EVAL_VIDXP_ON_WORKSPACE }}" + TMPDIR: "{{ env.VIDXP_EVAL_VIDXP_ON_WORKSPACE }}/tmp" cli_config: features: multi_agent: false mcp_servers: vidxp: command: "{{ env.VIDXP_MCP_COMMAND }}" + env: + VIDXP_MODEL_CACHE: "{{ env.VIDXP_MODEL_CACHE }}" + VIDXP_ALLOW_MODEL_DOWNLOADS: "false" + VIDXP_SLM_BASE_URL: "" + VIDXP_SLM_MODEL: "" args: - --repository - "{{ env.VIDXP_EVAL_REPOSITORY | default('default') }}" @@ -97,33 +98,60 @@ providers: - "{{ env.VIDXP_EVAL_DEVICE | default('cpu') }}" - id: openai:codex-sdk - label: codex-no-mcp + label: codex-baseline config: - model: "{{ env.VIDXP_EVAL_MODEL | default('gpt-5.6-sol') }}" - model_reasoning_effort: "{{ env.VIDXP_EVAL_REASONING | default('medium') }}" - maxRetries: 0 - working_dir: "{{ env.VIDXP_EVAL_WORKSPACE }}" - skip_git_repo_check: true - sandbox_mode: read-only - approval_policy: never - network_access_enabled: false - web_search_mode: disabled - persist_threads: false - enable_streaming: true - output_schema: *result_schema + <<: *vidxp_provider + working_dir: "{{ env.VIDXP_EVAL_VIDXP_OFF_WORKSPACE }}" + cli_env: + CODEX_HOME: "{{ env.VIDXP_EVAL_VIDXP_OFF_CODEX_HOME }}" + HOME: "{{ env.VIDXP_EVAL_VIDXP_OFF_WORKSPACE }}" + TMPDIR: "{{ env.VIDXP_EVAL_VIDXP_OFF_WORKSPACE }}/tmp" + cli_config: + features: + multi_agent: false + + - id: openai:codex-sdk + label: codex-clean-user + config: + <<: *vidxp_provider + working_dir: "{{ env.VIDXP_EVAL_CLEAN_USER_WORKSPACE }}" cli_env: - CODEX_HOME: "{{ env.VIDXP_EVAL_CODEX_HOME }}" + CODEX_HOME: "{{ env.VIDXP_EVAL_CLEAN_USER_CODEX_HOME }}" + HOME: "{{ env.VIDXP_EVAL_CLEAN_USER_WORKSPACE }}" + PATH: "{{ env.VIDXP_EVAL_CLEAN_USER_PATH }}" + TMPDIR: "{{ env.VIDXP_EVAL_CLEAN_USER_WORKSPACE }}/tmp" cli_config: features: multi_agent: false + - id: file://scripts/local_slm_provider.py:call_api + label: local-slm + config: &local_slm_provider + pythonExecutable: "{{ env.PROMPTFOO_PYTHON }}" + workers: 1 + timeout: 900000 + contextTokens: 64000 + maxOutputTokens: 32768 + modelTimeoutSeconds: 180 + strategy: router + + - id: file://scripts/local_slm_provider.py:call_api + label: local-slm-planner + config: + <<: *local_slm_provider + strategy: planner + tests: - path: file://../../src/vidxp/benchmarks/agent_ablation_tests.py:generate_tests config: manifest: tasks/longvale-part9-pilot.json + machine_id: "{{ env.VIDXP_EVAL_MACHINE_ID }}" providers: - mcp_on: codex-vidxp-mcp - mcp_off: codex-no-mcp + vidxp_on: codex-vidxp + vidxp_off: codex-baseline + clean_user: codex-clean-user + local_slm: local-slm + local_slm_planner: local-slm-planner evaluateOptions: cache: false diff --git a/benchmarks/codex-mcp/prompts/local-slm-planner.txt b/benchmarks/codex-mcp/prompts/local-slm-planner.txt new file mode 100644 index 00000000..0889f7dc --- /dev/null +++ b/benchmarks/codex-mcp/prompts/local-slm-planner.txt @@ -0,0 +1,20 @@ +Plan one ranked VidXP search for the supplied event. + +Return: + +- query: a concise search query that preserves every detail needed to + distinguish the event; rephrase only when it makes those details clearer +- modalities: every relevant indexed evidence type and no unrelated type +- candidate_top_k: the number of hits VidXP should retrieve from each selected + modality before fusion, from 3 through 100; use a deeper pool when the event + is ambiguous or likely to repeat + +The evidence types are: + +- scene: visible objects, setting, appearance, or visual state +- action: visible movement, activity, or change over time +- sound: non-speech audio, including environmental and mechanical sounds +- speech: spoken words or dialogue + +Do not predict timestamps, answer the request, or choose the final result count. +The harness always returns VidXP's top three fused evidence results. diff --git a/benchmarks/codex-mcp/prompts/local-slm-router.txt b/benchmarks/codex-mcp/prompts/local-slm-router.txt new file mode 100644 index 00000000..a47de71e --- /dev/null +++ b/benchmarks/codex-mcp/prompts/local-slm-router.txt @@ -0,0 +1,10 @@ +Choose which indexed VidXP evidence types are relevant to locating the supplied +event. Return every relevant type and no unrelated type. + +- scene: visible objects, setting, appearance, or visual state +- action: visible movement, activity, or change over time +- sound: non-speech audio, including environmental and mechanical sounds +- speech: spoken words or dialogue + +Do not locate the event, predict timestamps, rewrite the query, or summarize +results. Your only task is modality selection. diff --git a/benchmarks/codex-mcp/prompts/video-evidence.txt b/benchmarks/codex-mcp/prompts/video-evidence.txt index 2afc6d49..77c3c098 100644 --- a/benchmarks/codex-mcp/prompts/video-evidence.txt +++ b/benchmarks/codex-mcp/prompts/video-evidence.txt @@ -1,13 +1,21 @@ -Locate one event in the supplied video and return the single best time interval. +Locate one event in the supplied video and return up to three practical candidate +clips, ordered from most to least likely. Return fewer when the available evidence +does not support distinct alternatives. -Dataset: {{ dataset }} Video ID: {{ video_id }} -Media path: {{ env.VIDXP_EVAL_WORKSPACE }}/{{ media_relpath }} +Local media path, when available: {{ media_relpath }} Video duration: {{ duration_seconds }} seconds Event to locate: {{ query }} +Each clip: aim for {{ target_chunk_seconds }} seconds and keep it between +{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. The clip must contain +the event, but it does not need to trim the event's exact boundaries. For an +event longer than the target, choose its most representative target-size part. +Near the start or end of the video, shift the clip instead of shortening it. -Use the media and any assistant tools already available in this condition. Do -not use the network, read benchmark annotations, or invoke the VidXP CLI from -the shell. Base the result on inspected evidence rather than the filename or -query alone. If the evidence cannot be inspected, return null start and end -values and explain the limitation. Return only the requested JSON object. +Ground each candidate in available evidence rather than the filename or query +alone. Reuse evidence already returned by a tool; do not perform extra inspection +solely to reconfirm an already supported candidate. Preserve the source job and +candidate evidence IDs when an evidence source returns them; otherwise use an +empty evidence-ID list and set the source job to null. +If no candidate can be grounded, explain the limitation in the answer and return +an empty candidate list. Return only the requested JSON object. diff --git a/benchmarks/codex-mcp/run b/benchmarks/codex-mcp/run new file mode 100755 index 00000000..c4b6beba --- /dev/null +++ b/benchmarks/codex-mcp/run @@ -0,0 +1,152 @@ +#!/bin/sh + +set -eu + +benchmark_dir=$(CDPATH= cd -- "$(dirname -- "$0")" && pwd) + +if [ -x /opt/homebrew/opt/node@22/bin/node ]; then + node_dir=/opt/homebrew/opt/node@22/bin +elif [ -x /usr/local/opt/node@22/bin/node ]; then + node_dir=/usr/local/opt/node@22/bin +elif command -v node >/dev/null 2>&1; then + node_dir=$(dirname -- "$(command -v node)") +else + echo "Node.js 22.22.0 or newer is required." >&2 + exit 1 +fi + +PATH="$node_dir:$PATH" +export PATH + +cd "$benchmark_dir" +node scripts/require-node.mjs + +command=${1:-} +if [ "$#" -gt 0 ]; then + shift +fi + +case "$command" in + setup) + exec npm run setup -- "$@" + ;; + check) + exec npm run check -- "$@" + ;; + preflight) + exec npm run preflight -- "$@" + ;; + smoke) + exec npm run eval:smoke + ;; + pilot) + case "${1:-}" in + "") + ;; + *[!0-9]*|0) + echo "Pilot repetitions must be a positive integer." >&2 + exit 2 + ;; + *) + VIDXP_EVAL_REPETITIONS=$1 + export VIDXP_EVAL_REPETITIONS + shift + ;; + esac + if [ "$#" -gt 0 ]; then + echo "Usage: ./benchmarks/codex-mcp/run pilot [repetitions]" >&2 + exit 2 + fi + exec npm run eval:pilot + ;; + vidxp) + case "${1:-3}" in + *[!0-9]*|0) + echo "VidXP repetitions must be a positive integer." >&2 + exit 2 + ;; + esac + repetitions=${1:-3} + if [ "$#" -gt 1 ]; then + echo "Usage: ./benchmarks/codex-mcp/run vidxp [repetitions]" >&2 + exit 2 + fi + VIDXP_EVAL_REPETITIONS=$repetitions + export VIDXP_EVAL_REPETITIONS + exec npm run eval:vidxp + ;; + indexing) + case "${1:-3}" in + *[!0-9]*|0) + echo "Indexing repetitions must be a positive integer." >&2 + exit 2 + ;; + esac + repetitions=${1:-3} + if [ "$#" -gt 1 ]; then + echo "Usage: ./benchmarks/codex-mcp/run indexing [repetitions]" >&2 + exit 2 + fi + exec node --env-file=.env --no-warnings scripts/indexing-benchmark.mjs "$repetitions" + ;; + slm) + case "${1:-3}" in + *[!0-9]*|0) + echo "SLM repetitions must be a positive integer." >&2 + exit 2 + ;; + esac + repetitions=${1:-3} + if [ "$#" -gt 1 ]; then + echo "Usage: ./benchmarks/codex-mcp/run slm [repetitions]" >&2 + exit 2 + fi + VIDXP_EVAL_REPETITIONS=$repetitions + export VIDXP_EVAL_REPETITIONS + exec npm run eval:slm + ;; + slm-smoke) + if [ "$#" -gt 0 ]; then + echo "Usage: ./benchmarks/codex-mcp/run slm-smoke" >&2 + exit 2 + fi + exec npm run eval:slm:smoke + ;; + results) + exec npm run report -- "$@" + ;; + export) + exec npm run export -- "$@" + ;; + trace) + exec npm run report -- --retrieval "$@" + ;; + probe) + exec "$benchmark_dir/../../.venv/bin/python" scripts/modality_probe.py "$@" + ;; + depth) + exec "$benchmark_dir/../../.venv/bin/python" scripts/candidate_depth_control.py "$@" + ;; + compare) + exec "$benchmark_dir/../../.venv/bin/python" scripts/compare_point_to_span.py "$@" + ;; + representation) + exec "$benchmark_dir/../../.venv/bin/python" scripts/action_representation.py "$@" + ;; + shots) + exec "$benchmark_dir/../../.venv/bin/python" scripts/shot_proposal_control.py "$@" + ;; + queries) + exec "$benchmark_dir/../../.venv/bin/python" scripts/query_routing_control.py "$@" + ;; + sound) + exec "$benchmark_dir/../../.venv/bin/python" scripts/sound_two_stage.py "$@" + ;; + view) + exec npm run promptfoo -- view --yes "$@" + ;; + *) + echo "Usage: ./benchmarks/codex-mcp/run {setup|check|preflight|smoke|pilot [repetitions]|vidxp [repetitions]|slm-smoke|slm [repetitions]|indexing [repetitions]|results|export|trace|probe|depth|compare|representation|shots|queries|sound|view}" >&2 + exit 2 + ;; +esac diff --git a/benchmarks/codex-mcp/scripts/action_representation.py b/benchmarks/codex-mcp/scripts/action_representation.py new file mode 100644 index 00000000..1547bbde --- /dev/null +++ b/benchmarks/codex-mcp/scripts/action_representation.py @@ -0,0 +1,701 @@ +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import sys +import time +from datetime import datetime +from pathlib import Path +from typing import Any + +from vidxp.application_models import ( + CreateIndexCommand, + ImportMediaCommand, + ListMediaCommand, + MediaState, + SearchHit, + SearchResult, +) +from vidxp.benchmarks.agent_ablation_score import interval_iou +from vidxp.capabilities.action.config import videoprism_config +from vidxp.composition import create_local_application +from vidxp.index_state import IndexNotReadyError +from vidxp.search_fusion import fuse_search_results + +from modality_probe import ( + _load_environment, + _output_path, + _required_environment, + _search_all, + _task, +) + + +def _profile(sample_fps: float, stride_samples: int) -> tuple[str, dict[str, Any]]: + settings = { + "sample_fps": sample_fps, + "clip_stride_samples": stride_samples, + } + encoded = json.dumps(settings, sort_keys=True, separators=(",", ":")).encode() + return f"videoprism-{hashlib.sha256(encoded).hexdigest()[:12]}", settings + + +def _directory_size(path: Path) -> int: + return sum(item.stat().st_size for item in path.rglob("*") if item.is_file()) + + +def _generation_metrics( + index_directory: Path, + *, + snapshot_id: str, + media_id: str, +) -> dict[str, Any]: + indexes = index_directory / "indexes" + snapshot = json.loads( + (indexes / "snapshots" / f"{snapshot_id}.json").read_text( + encoding="utf-8" + ) + ) + reference = snapshot["generations"][media_id] + manifest = json.loads( + ( + indexes + / "generations" + / reference["generation_id"] + / "manifest.json" + ).read_text(encoding="utf-8") + ) + video = manifest["videos"][media_id] + created = datetime.fromisoformat(manifest["created_at"]) + completed = datetime.fromisoformat(manifest["completed_at"]) + return { + "generation_id": reference["generation_id"], + "generation_wall_seconds": (completed - created).total_seconds(), + "visual_indexing_seconds": video["stages"]["visual_indexing"]["seconds"], + "committed_generation_bytes": reference["store_size_bytes_at_commit"], + } + + +def _metrics(start: float, end: float, task: dict[str, Any]) -> dict[str, float]: + expected_start = float(task["expected_start"]) + expected_end = float(task["expected_end"]) + return { + "temporal_iou": interval_iou(start, end, expected_start, expected_end), + "start_error_seconds": start - expected_start, + "end_error_seconds": end - expected_end, + "duration_error_seconds": (end - start) - (expected_end - expected_start), + } + + +def _ranked_records(probe: dict[str, Any]) -> list[dict[str, Any]]: + return sorted(probe["records"], key=lambda record: record["retrieval_rank"]) + + +def _record_metrics( + record: dict[str, Any], + task: dict[str, Any], +) -> dict[str, Any]: + result = { + "start_seconds": record["start_seconds"], + "end_seconds": record["end_seconds"], + "retrieval_rank": record["retrieval_rank"], + **_metrics( + float(record["start_seconds"]), + float(record["end_seconds"]), + task, + ), + } + if "coarse_parent_ranks" in record: + result["coarse_parent_ranks"] = record["coarse_parent_ranks"] + return result + + +def _candidate_summary( + records: list[dict[str, Any]], + task: dict[str, Any], + *, + top_k: int, +) -> dict[str, Any]: + if not records: + raise RuntimeError("the action comparison has no candidate records") + top_records = records[:top_k] + top_metrics = [_record_metrics(record, task) for record in top_records] + all_metrics = [_record_metrics(record, task) for record in records] + return { + "top_retrieved": top_metrics[0], + "top_k": top_metrics, + "best_in_top_k": max( + top_metrics, + key=lambda item: item["temporal_iou"], + ), + "best_candidate_oracle": max( + all_metrics, + key=lambda item: item["temporal_iou"], + ), + "candidate_count": len(records), + } + + +def _coarse_to_fine_summary( + coarse_probe: dict[str, Any], + fine_probe: dict[str, Any], + task: dict[str, Any], + *, + top_k: int, +) -> dict[str, Any]: + coarse = _ranked_records(coarse_probe)[:top_k] + fine = _ranked_records(fine_probe) + selected = [] + for record in fine: + midpoint = ( + float(record["start_seconds"]) + float(record["end_seconds"]) + ) / 2.0 + parent_ranks = [ + parent["retrieval_rank"] + for parent in coarse + if float(parent["start_seconds"]) + <= midpoint + <= float(parent["end_seconds"]) + ] + if parent_ranks: + selected.append({**record, "coarse_parent_ranks": parent_ranks}) + summary = _candidate_summary(selected, task, top_k=top_k) + summary["coarse_gate"] = _candidate_summary(coarse, task, top_k=top_k) + summary["gate_rule"] = ( + "fine-window midpoint falls inside any of the top-k coarse windows" + ) + summary["boundary_rule"] = "return one ranked fine window without union" + return summary + + +def _saved_result( + modality: str, + probe: dict[str, Any], + *, + top_k: int, +) -> SearchResult: + records = sorted( + probe["modalities"][modality]["records"], + key=lambda record: record["retrieval_rank"], + )[:top_k] + hits = tuple( + SearchHit( + rank=record["retrieval_rank"], + media_id=probe["media_id"], + video_id=probe["media_id"], + generation_id=record["source_id"].split(":", 1)[0], + start=record["start_seconds"], + end=record["end_seconds"], + score=record["ordering_score"], + raw_distance=record["raw_distance"], + modality=modality, + source_id=record["source_id"], + metadata=record["metadata"], + ) + for record in records + ) + return SearchResult( + query_id=f"saved:{probe['task_id']}:{modality}", + query=probe["query"], + modality=modality, + hits=hits, + ) + + +def compare_action_representation( + task_id: str, + *, + sample_fps: float, + stride_samples: int, +) -> dict[str, Any]: + if sample_fps <= 0: + raise ValueError("sample_fps must be positive") + if not 1 <= stride_samples <= 16: + raise ValueError("stride_samples must be between 1 and 16") + + _load_environment() + task = _task(task_id) + if "action" not in task["modalities"]: + raise ValueError(f"task does not declare action evidence: {task_id}") + base_path = _output_path(task_id, None) + if not base_path.is_file(): + raise RuntimeError(f"run './benchmarks/codex-mcp/run probe {task_id}' first") + base_probe = json.loads(base_path.read_text(encoding="utf-8")) + top_k = int(base_probe["current_control"]["candidate_top_k_per_modality"]) + + profile, options = _profile(sample_fps, stride_samples) + evaluation_root = Path(_required_environment("VIDXP_EVAL_DATA_DIR")).parent + profile_root = evaluation_root / "action-representations" / profile + data_directory = profile_root / "data" + index_directory = profile_root / "index" + source = Path(_required_environment("VIDXP_EVAL_WORKSPACE")) / task["media_relpath"] + if not source.is_file(): + raise RuntimeError(f"prepared benchmark media is missing: {source}") + + context = create_local_application( + repository_name=os.environ.get("VIDXP_EVAL_REPOSITORY", "default"), + index_directory=index_directory, + data_directory=data_directory, + device=os.environ.get("VIDXP_EVAL_DEVICE", "cpu"), + ) + indexing_seconds = 0.0 + reused_index = False + try: + application = context.application + page = application.media.list( + ListMediaCommand( + page_size=2, + filename=source.name, + state=MediaState.ready, + ) + ) + if len(page.items) > 1: + raise RuntimeError( + f"multiple experimental media records match {source.name}" + ) + media = ( + page.items[0] + if page.items + else application.import_media(ImportMediaCommand(path=source)) + ) + + try: + config = application.index_backend.active_config( + application.index_directory, + device=application.device, + ) + except IndexNotReadyError: + config = None + if config is not None: + effective = videoprism_config(config) + if ( + effective.sample_fps != sample_fps + or effective.clip_stride_samples != stride_samples + ): + raise RuntimeError( + f"experimental profile {profile} has different settings" + ) + with application.index_backend.open_store(config) as storage: + reused_index = storage.count_records( + "action", video_id=media.media_id + ) > 0 + + if not reused_index: + print( + f"Indexing fine action windows for {task_id}...", + file=sys.stderr, + flush=True, + ) + started = time.perf_counter() + application.create_index( + CreateIndexCommand( + media_id=media.media_id, + modalities=("action",), + capability_options={"action": options}, + ) + ) + indexing_seconds = time.perf_counter() - started + print( + f"Indexed fine action windows in {indexing_seconds:.3f}s.", + file=sys.stderr, + flush=True, + ) + config = application.index_backend.active_config( + application.index_directory, + device=application.device, + ) + assert config is not None + + with application.index_backend.open_store(config) as storage: + with application.runtime.scheduler.inference(): + action_result, action_probe = _search_all( + "action", + task["query"], + media.media_id, + float(task["expected_start"]), + float(task["expected_end"]), + config=config, + runtime=application.runtime, + storage=storage, + ) + finally: + context.close() + + normalized_action = action_result.model_copy( + update={ + "hits": tuple( + hit.model_copy( + update={ + "media_id": base_probe["media_id"], + "video_id": base_probe["media_id"], + } + ) + for hit in action_result.hits[:top_k] + ) + } + ) + results = tuple( + normalized_action + if modality == "action" + else _saved_result(modality, base_probe, top_k=top_k) + for modality in task["modalities"] + if modality == "action" or modality in base_probe["modalities"] + ) + fused = fuse_search_results( + query=task["query"], + requested_modalities=tuple(task["modalities"]), + results=results, + media_id=base_probe["media_id"], + top_k=top_k, + snapshot_id=base_probe["snapshot_id"], + ) + top_moment = fused.moments[0] if fused.moments else None + control_action_records = int( + base_probe["modalities"]["action"]["record_count"] + ) + action_records = int(action_probe["record_count"]) + generation_metrics = _generation_metrics( + index_directory, + snapshot_id=config.snapshot_id, + media_id=media.media_id, + ) + coarse_probe = base_probe["modalities"]["action"] + comparison = { + "current_coarse": _candidate_summary( + _ranked_records(coarse_probe), + task, + top_k=top_k, + ), + "fine_only": _candidate_summary( + _ranked_records(action_probe), + task, + top_k=top_k, + ), + "coarse_to_fine": _coarse_to_fine_summary( + coarse_probe, + action_probe, + task, + top_k=top_k, + ), + } + output = profile_root / f"{task_id}.json" + payload = { + "schema_version": 2, + "task_id": task_id, + "profile": profile, + "research_role": ( + "overlapping fixed-window control; exact settings are VidXP experimental" + ), + "settings": { + **options, + "nominal_window_seconds": 16 / sample_fps, + "nominal_stride_seconds": stride_samples / sample_fps, + }, + "control": base_probe["current_control"], + "experimental": { + "action": action_probe, + "fused_result": fused.model_dump(mode="json"), + "top_moment_metrics": ( + _metrics(top_moment.start, top_moment.end, task) + if top_moment is not None + else None + ), + }, + "multiscale_comparison": comparison, + "resource_use": { + "index_reused": reused_index, + "indexing_seconds_this_run": indexing_seconds, + "profile_store_bytes": _directory_size(index_directory), + **generation_metrics, + "control_action_record_count": control_action_records, + "action_record_count": action_records, + "action_record_count_multiplier": action_records / control_action_records, + "query_seconds": action_probe["elapsed_seconds"], + "model_calls": { + "action_video_embedding_batches": ( + 0 if reused_index else action_records + ), + "action_text_embedding": 1, + }, + }, + } + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text( + json.dumps(payload, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + return { + "output": str(output), + "profile": profile, + "settings": payload["settings"], + "control": base_probe["current_control"]["top_moment_metrics"], + "experimental": payload["experimental"]["top_moment_metrics"], + "action_top_retrieved": action_probe["top_retrieved"], + "action_best_individual_interval_oracle": action_probe[ + "best_individual_interval_oracle" + ], + "multiscale_comparison": comparison, + "resource_use": payload["resource_use"], + } + + +def _method_summary(results: list[dict[str, Any]], method: str) -> dict[str, Any]: + top = [ + result["multiscale_comparison"][method]["top_retrieved"] + for result in results + ] + best_top_k = [ + result["multiscale_comparison"][method]["best_in_top_k"] + for result in results + ] + oracle = [ + result["multiscale_comparison"][method]["best_candidate_oracle"] + for result in results + ] + + def rates(values: list[dict[str, Any]]) -> dict[str, float]: + return { + f"tiou_{threshold}": sum( + item["temporal_iou"] >= threshold for item in values + ) + / len(values) + for threshold in (0.3, 0.5, 0.7) + } + + return { + "tasks": len(results), + "mean_top1_iou": sum(item["temporal_iou"] for item in top) / len(top), + "top1_threshold_rates": rates(top), + "top_k_candidate_recall": rates(best_top_k), + "oracle_threshold_rates": rates(oracle), + "mean_best_in_top_k_iou": sum( + item["temporal_iou"] for item in best_top_k + ) + / len(best_top_k), + "mean_oracle_iou": sum(item["temporal_iou"] for item in oracle) + / len(oracle), + "mean_absolute_start_error_seconds": sum( + abs(item["start_error_seconds"]) for item in top + ) + / len(top), + "mean_absolute_end_error_seconds": sum( + abs(item["end_error_seconds"]) for item in top + ) + / len(top), + } + + +def compare_held_out( + *, + sample_fps: float, + stride_samples: int, +) -> dict[str, Any]: + _load_environment() + tasks = json.loads( + ( + Path(__file__).resolve().parent.parent + / "tasks" + / "longvale-part9-pilot.json" + ).read_text(encoding="utf-8") + ) + selected = [task for task in tasks[2:] if "action" in task["modalities"]] + missing = [ + task["id"] + for task in selected + if not _output_path(task["id"], None).is_file() + ] + if missing: + raise RuntimeError( + "missing held-out probes; run './benchmarks/codex-mcp/run probe " + f"TASK_ID' for: {', '.join(missing)}" + ) + + results = [] + for index, task in enumerate(selected, start=1): + print( + f"[{index}/{len(selected)}] {task['id']}", + file=sys.stderr, + flush=True, + ) + results.append( + compare_action_representation( + task["id"], + sample_fps=sample_fps, + stride_samples=stride_samples, + ) + ) + methods = { + method: _method_summary(results, method) + for method in ("current_coarse", "fine_only", "coarse_to_fine") + } + unique_video_resources: dict[str, dict[str, Any]] = {} + for task, result in zip(selected, results): + unique_video_resources.setdefault(task["video_id"], result["resource_use"]) + + profile, settings = _profile(sample_fps, stride_samples) + evaluation_root = Path(_required_environment("VIDXP_EVAL_DATA_DIR")).parent + output = evaluation_root / "action-representations" / profile / "held-out.json" + aggregate = { + "schema_version": 1, + "scope": "five frozen held-out action tasks across three videos", + "task_ids": [task["id"] for task in selected], + "method": { + "research_basis": [ + "CTAP (Gao et al., ECCV 2018)", + ( + "Localizing Moments in Long Video via Multimodal Guidance " + "(Barrios et al., ICCV 2023)" + ), + ], + "vidxp_choices": { + **settings, + "coarse_top_k": 3, + "gate_rule": ( + "fine-window midpoint falls inside any top-three coarse window" + ), + "boundary_rule": "return one ranked fine window without union", + }, + "excluded": [ + "multimodal fusion", + "query rewriting", + "agent or MCP execution", + "learned boundary prediction", + ], + }, + "methods": methods, + "per_task": [ + { + "task_id": task["id"], + "expected_start": task["expected_start"], + "expected_end": task["expected_end"], + "current_coarse": result["multiscale_comparison"][ + "current_coarse" + ], + "fine_only": result["multiscale_comparison"]["fine_only"], + "coarse_to_fine": result["multiscale_comparison"][ + "coarse_to_fine" + ], + } + for task, result in zip(selected, results) + ], + "resource_use": { + "unique_videos": len(unique_video_resources), + "fine_indexing_seconds_this_run": sum( + resource["indexing_seconds_this_run"] + for resource in unique_video_resources.values() + ), + "recorded_generation_wall_seconds": sum( + resource["generation_wall_seconds"] + for resource in unique_video_resources.values() + ), + "recorded_visual_indexing_seconds": sum( + resource["visual_indexing_seconds"] + for resource in unique_video_resources.values() + ), + "fine_action_records": sum( + resource["action_record_count"] + for resource in unique_video_resources.values() + ), + "current_action_records": sum( + resource["control_action_record_count"] + for resource in unique_video_resources.values() + ), + "fine_generation_bytes": sum( + resource["committed_generation_bytes"] + for resource in unique_video_resources.values() + ), + "profile_store_bytes": max( + resource["profile_store_bytes"] + for resource in unique_video_resources.values() + ), + "new_fine_text_embedding_calls": len(results), + "coarse_probe_results_reused": True, + "live_product_text_embedding_calls_per_task": 2, + "codex_calls": 0, + "api_calls": 0, + }, + } + output.write_text( + json.dumps(aggregate, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + return {**aggregate, "output": str(output)} + + +def _print_held_out(result: dict[str, Any]) -> None: + print("Held-out VideoPrism multiscale comparison") + print("Method top1 IoU >=.3 >=.5 >=.7 top3@.5 oracle@.5") + for key, label in ( + ("current_coarse", "Current 8-second"), + ("fine_only", "Fine-only"), + ("coarse_to_fine", "Coarse-to-fine"), + ): + metrics = result["methods"][key] + top1 = metrics["top1_threshold_rates"] + top_k = metrics["top_k_candidate_recall"] + oracle = metrics["oracle_threshold_rates"] + print( + f"{label:<18} {metrics['mean_top1_iou']:>8.4f} " + f"{top1['tiou_0.3']:>7.3f} {top1['tiou_0.5']:>7.3f} " + f"{top1['tiou_0.7']:>7.3f} {top_k['tiou_0.5']:>9.3f} " + f"{oracle['tiou_0.5']:>10.3f}" + ) + print("\nTask current fine coarseā†’fine") + for task in result["per_task"]: + print( + f"{task['task_id'].removeprefix('longvale-part9-'):<29} " + f"{task['current_coarse']['top_retrieved']['temporal_iou']:>7.4f} " + f"{task['fine_only']['top_retrieved']['temporal_iou']:>7.4f} " + f"{task['coarse_to_fine']['top_retrieved']['temporal_iou']:>13.4f}" + ) + resources = result["resource_use"] + print( + "\nResource use: " + f"{resources['fine_action_records']} fine records versus " + f"{resources['current_action_records']} current records; " + f"{resources['recorded_generation_wall_seconds']:.3f}s recorded build time; " + f"{resources['new_fine_text_embedding_calls']} new local text embeddings; " + "0 Codex/API calls." + ) + print(f"Full evidence: {result['output']}") + + +def main() -> int: + parser = argparse.ArgumentParser( + description=( + "Index and compare one isolated overlapping VideoPrism representation." + ) + ) + parser.add_argument("task_id", nargs="?") + parser.add_argument("--held-out", action="store_true") + parser.add_argument("--sample-fps", type=float, required=True) + parser.add_argument("--stride-samples", type=int, required=True) + arguments = parser.parse_args() + if arguments.held_out == (arguments.task_id is not None): + parser.error("provide one task ID or --held-out") + if arguments.held_out: + _print_held_out( + compare_held_out( + sample_fps=arguments.sample_fps, + stride_samples=arguments.stride_samples, + ) + ) + else: + print( + json.dumps( + compare_action_representation( + arguments.task_id, + sample_fps=arguments.sample_fps, + stride_samples=arguments.stride_samples, + ), + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/candidate_depth_control.py b/benchmarks/codex-mcp/scripts/candidate_depth_control.py new file mode 100644 index 00000000..335b08f6 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/candidate_depth_control.py @@ -0,0 +1,275 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +from modality_probe import _load_environment, _output_path +from vidxp.application_models import SearchHit, SearchResult +from vidxp.benchmarks.agent_ablation_score import interval_iou +from vidxp.search_fusion import fuse_search_results + + +TASKS_PATH = ( + Path(__file__).resolve().parent.parent + / "tasks" + / "longvale-part9-pilot.json" +) +DEPTHS = (1, 3, 5, 10, 20, 50, 100, 250, 500, 1000) +THRESHOLDS = (0.3, 0.5, 0.7) +OUTPUT_TOP_K = 10 +BOARD_TOP_K = 3 +EVALUATION_TOP_K = 5 + + +def _saved_result( + probe: dict[str, Any], + modality: str, + depth: int, +) -> SearchResult: + records = sorted( + probe["modalities"][modality]["records"], + key=lambda record: record["retrieval_rank"], + )[:depth] + return SearchResult( + query_id=f"candidate-depth:{probe['task_id']}:{modality}:{depth}", + query=probe["query"], + modality=modality, + hits=tuple( + SearchHit( + rank=record["retrieval_rank"], + media_id=probe["media_id"], + video_id=probe["media_id"], + generation_id=record["source_id"].split(":", 1)[0], + start=record["start_seconds"], + end=record["end_seconds"], + score=record["ordering_score"], + raw_distance=record["raw_distance"], + modality=modality, + source_id=record["source_id"], + metadata=record["metadata"], + ) + for record in records + ), + ) + + +def _moment_metrics( + moments: tuple[Any, ...], + expected_start: float, + expected_end: float, +) -> dict[str, Any]: + ious = [ + interval_iou(moment.start, moment.end, expected_start, expected_end) + for moment in moments + ] + best_index = max(range(len(ious)), key=ious.__getitem__) if ious else None + return { + "top_interval": ( + { + "start_seconds": moments[0].start, + "end_seconds": moments[0].end, + } + if moments + else None + ), + "top_1_iou": ious[0] if ious else 0.0, + "best_board_iou": max(ious[:BOARD_TOP_K], default=0.0), + "best_top_5_iou": max(ious[:EVALUATION_TOP_K], default=0.0), + "best_output_iou": max(ious, default=0.0), + "best_output_rank": best_index + 1 if best_index is not None else None, + "top_candidates": [ + { + "rank": moment.rank, + "start_seconds": moment.start, + "end_seconds": moment.end, + "iou": iou, + "score": moment.score, + "evidence": [ + { + "modality": hit.modality, + "rank": hit.rank, + "start_seconds": hit.start, + "end_seconds": hit.end, + } + for hit in moment.hits + ], + } + for moment, iou in zip(moments, ious) + ], + "returned_moments": len(moments), + } + + +def _task_depth_result(probe: dict[str, Any], depth: int) -> dict[str, Any]: + modalities = tuple(probe["modalities"]) + fused = fuse_search_results( + query=probe["query"], + requested_modalities=modalities, + results=tuple( + _saved_result(probe, modality, depth) for modality in modalities + ), + media_id=probe["media_id"], + top_k=OUTPUT_TOP_K, + snapshot_id=probe["snapshot_id"], + ) + return _moment_metrics( + fused.moments, + float(probe["expected_start"]), + float(probe["expected_end"]), + ) + + +def _aggregate(task_results: list[dict[str, Any]]) -> dict[str, Any]: + count = len(task_results) + return { + "tasks": count, + "mean_top_1_iou": sum(item["top_1_iou"] for item in task_results) + / count, + "mean_best_board_iou": sum( + item["best_board_iou"] for item in task_results + ) + / count, + "mean_best_top_5_iou": sum( + item["best_top_5_iou"] for item in task_results + ) + / count, + "mean_best_output_iou": sum( + item["best_output_iou"] for item in task_results + ) + / count, + "recall_at_1": { + str(threshold): sum( + item["top_1_iou"] >= threshold for item in task_results + ) + / count + for threshold in THRESHOLDS + }, + "recall_at_board_3": { + str(threshold): sum( + item["best_board_iou"] >= threshold for item in task_results + ) + / count + for threshold in THRESHOLDS + }, + "recall_at_5": { + str(threshold): sum( + item["best_top_5_iou"] >= threshold for item in task_results + ) + / count + for threshold in THRESHOLDS + }, + "recall_at_output_10": { + str(threshold): sum( + item["best_output_iou"] >= threshold for item in task_results + ) + / count + for threshold in THRESHOLDS + }, + } + + +def compare_candidate_depths(output: Path | None = None) -> dict[str, Any]: + _load_environment() + tasks = json.loads(TASKS_PATH.read_text(encoding="utf-8")) + probes = [] + for task in tasks: + path = _output_path(task["id"], None) + if not path.is_file(): + raise RuntimeError( + "saved full-list probe is missing; run " + f"'./benchmarks/codex-mcp/run probe {task['id']}' first" + ) + probes.append(json.loads(path.read_text(encoding="utf-8"))) + + maximum_records = max( + modality["record_count"] + for probe in probes + for modality in probe["modalities"].values() + ) + depths = (*DEPTHS, maximum_records) + per_depth: dict[str, Any] = {} + for depth in depths: + task_results = [ + { + "task_id": probe["task_id"], + **_task_depth_result(probe, depth), + } + for probe in probes + ] + label = "all" if depth == maximum_records else str(depth) + per_depth[label] = { + "candidate_depth_per_modality": ( + "all available records" if label == "all" else depth + ), + "aggregate": _aggregate(task_results), + "tasks": task_results, + } + + payload = { + "schema_version": 2, + "control_id": "candidate-depth-direct-overlap-control-v2", + "control": ( + "Replay saved full-query modality rankings through the production " + "rank-anchored direct-overlap RRF implementation. Vary only the maximum " + "number of candidates retained per modality." + ), + "task_count": len(probes), + "output_top_k": OUTPUT_TOP_K, + "evidence_board_top_k": BOARD_TOP_K, + "depths": per_depth, + "notes": [ + "Depth values are curve samples, not proposed product defaults.", + "No model inference, agent run, or API call is made.", + "The all-records point checks that additional candidates do not " + "expand a result through transitive overlap.", + ], + } + destination = output + if destination is None: + destination = _output_path("candidate-depth-control", None).with_name( + "candidate-depth-direct-overlap-control.json" + ) + destination = destination.resolve() + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text( + json.dumps(payload, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + return payload + + +def _print_summary(payload: dict[str, Any]) -> None: + print( + "depth mean@1 R1@.3/.5/.7 " + "Rboard3@.3/.5/.7 R5@.3/.5/.7 R10@.3/.5/.7" + ) + for label, result in payload["depths"].items(): + aggregate = result["aggregate"] + r1 = aggregate["recall_at_1"] + board = aggregate["recall_at_board_3"] + top_5 = aggregate["recall_at_5"] + output = aggregate["recall_at_output_10"] + print( + f"{label:>5} {aggregate['mean_top_1_iou']:.4f} " + f"{r1['0.3']:.2f}/{r1['0.5']:.2f}/{r1['0.7']:.2f} " + f"{board['0.3']:.2f}/{board['0.5']:.2f}/{board['0.7']:.2f} " + f"{top_5['0.3']:.2f}/{top_5['0.5']:.2f}/{top_5['0.7']:.2f} " + f"{output['0.3']:.2f}/{output['0.5']:.2f}/{output['0.7']:.2f}" + ) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Replay saved rankings at independent candidate depths." + ) + parser.add_argument("--output", type=Path) + arguments = parser.parse_args() + payload = compare_candidate_depths(arguments.output) + _print_summary(payload) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/compare_point_to_span.py b/benchmarks/codex-mcp/scripts/compare_point_to_span.py new file mode 100644 index 00000000..9c294791 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/compare_point_to_span.py @@ -0,0 +1,315 @@ +from __future__ import annotations + +import argparse +import json +import time +from pathlib import Path +from typing import Any + +from modality_probe import _load_environment, _output_path +from vidxp.application_models import SearchHit, SearchResult +from vidxp.benchmarks.agent_ablation_score import interval_iou +from vidxp.benchmarks.point_to_span import ( + MINIMUM_PEAK_DISTANCE_SECONDS, + NMS_TIOU_THRESHOLD, + PAPER_URL, + PEAK_PROMINENCE, + TemporalSimilarity, + adaptive_span_generator, + squared_l2_to_cosine, +) +from vidxp.search_fusion import fuse_search_results + + +def _comparison_path(probe_path: Path, requested: Path | None) -> Path: + if requested is not None: + return requested.resolve() + return probe_path.with_name(probe_path.name.replace(".probe.json", ".p2s.json")) + + +def _timeline_records( + modality: str, + records: list[dict[str, Any]], +) -> tuple[list[dict[str, Any]], str]: + if modality == "sound": + selected = [ + record + for record in records + if record["metadata"].get("representation") == "activation" + ] + return selected, "FineLAP activation records only" + return records, "all indexed records" + + +def _metrics( + start: float, + end: float, + *, + expected_start: float, + expected_end: float, +) -> dict[str, float]: + return { + "temporal_iou": interval_iou(start, end, expected_start, expected_end), + "start_error_seconds": start - expected_start, + "end_error_seconds": end - expected_end, + "duration_error_seconds": ( + end - start - expected_end + expected_start + ), + } + + +def _generation_id(records: list[dict[str, Any]]) -> str: + components = records[0]["source_id"].split(":") + if len(components) < 4: + raise ValueError("probe source ID does not contain an index generation") + return components[0] + + +def compare_probe( + probe_path: Path, + output: Path | None = None, +) -> dict[str, Any]: + probe = json.loads(probe_path.read_text(encoding="utf-8")) + if probe.get("vector_distance") != "l2": + raise ValueError("the current comparison requires squared L2 distances") + expected_start = float(probe["expected_start"]) + expected_end = float(probe["expected_end"]) + output_top_k = int(probe["current_control"]["output_top_k"]) + modality_reports: dict[str, Any] = {} + span_results = [] + started = time.perf_counter() + + for modality, modality_probe in probe["modalities"].items(): + source_records = modality_probe["records"] + if modality == "speech": + selected = sorted( + source_records, + key=lambda record: record["retrieval_rank"], + )[:output_top_k] + modality_reports[modality] = { + "status": "passthrough", + "reason": "speech records already contain semantic timestamps", + "record_count": len(source_records), + "candidates": [ + { + "rank": record["retrieval_rank"], + "start_seconds": record["start_seconds"], + "end_seconds": record["end_seconds"], + } + for record in selected + ], + } + span_results.append( + SearchResult( + query_id=f"p2s-asg:speech:{probe['task_id']}", + query=probe["query"], + modality=modality, + hits=tuple( + SearchHit( + rank=rank, + media_id=probe["media_id"], + video_id=probe["media_id"], + generation_id=_generation_id(source_records), + start=record["start_seconds"], + end=record["end_seconds"], + score=record["ordering_score"], + raw_distance=record["raw_distance"], + modality=modality, + source_id=record["source_id"], + metadata=record["metadata"], + ) + for rank, record in enumerate(selected, start=1) + ), + ) + ) + continue + records, selection = _timeline_records(modality, source_records) + if not records: + modality_reports[modality] = { + "status": "not_applicable", + "reason": selection, + } + continue + sequence = tuple( + TemporalSimilarity( + start=float(record["start_seconds"]), + end=float(record["end_seconds"]), + similarity=squared_l2_to_cosine(record["raw_distance"]), + ) + for record in records + ) + result = adaptive_span_generator(sequence) + candidates = [ + { + "rank": rank, + "start_seconds": candidate.start, + "end_seconds": candidate.end, + "score": candidate.score, + "peak_time_seconds": candidate.peak_time, + "peak_similarity": candidate.peak_similarity, + "expansion_threshold": candidate.expansion_threshold, + } + for rank, candidate in enumerate(result.candidates, start=1) + ] + modality_reports[modality] = { + "status": "ok" if candidates else "no_candidates", + "record_selection": selection, + "record_count": len(records), + "sample_rate_hz": result.sample_rate_hz, + "signal_standard_deviation": result.signal_standard_deviation, + "adaptive_ratio": result.adaptive_ratio, + "smoothing_window_samples": result.smoothing_window_samples, + "candidates": candidates, + } + hits = tuple( + SearchHit( + rank=candidate["rank"], + media_id=probe["media_id"], + video_id=probe["media_id"], + generation_id=_generation_id(source_records), + start=candidate["start_seconds"], + end=candidate["end_seconds"], + score=candidate["score"], + raw_distance=2.0 * (1.0 - candidate["score"]), + modality=modality, + source_id=f"p2s-asg:{modality}:{candidate['rank']}", + metadata={"localizer": "p2s_asg_vidxp_v1"}, + ) + for candidate in candidates + ) + span_results.append( + SearchResult( + query_id=f"p2s-asg:{modality}:{probe['task_id']}", + query=probe["query"], + modality=modality, + hits=hits, + ) + ) + + fused = fuse_search_results( + query=probe["query"], + requested_modalities=tuple(result.modality for result in span_results), + results=tuple(span_results), + media_id=probe["media_id"], + top_k=output_top_k, + snapshot_id=probe["snapshot_id"], + ) + top_moment = fused.moments[0] if fused.moments else None + adapted_metrics = ( + _metrics( + top_moment.start, + top_moment.end, + expected_start=expected_start, + expected_end=expected_end, + ) + if top_moment is not None + else None + ) + elapsed_seconds = time.perf_counter() - started + payload = { + "schema_version": 1, + "task_id": probe["task_id"], + "probe": str(probe_path.resolve()), + "expected_interval": { + "start_seconds": expected_start, + "end_seconds": expected_end, + }, + "method": { + "id": "p2s_asg_vidxp_v1", + "paper": PAPER_URL, + "paper_component": ( + "Adaptive Span Generator, Section 3.1, with the published " + "final NMS setting" + ), + "published_settings": { + "peak_prominence": PEAK_PROMINENCE, + "minimum_peak_distance_seconds": MINIMUM_PEAK_DISTANCE_SECONDS, + "nms_tiou_threshold": NMS_TIOU_THRESHOLD, + }, + "not_implemented": [ + "LLM query decomposition", + "evidence-based reranking", + "evidence-union injection", + ], + "vidxp_adaptations": [ + "use existing VideoPrism, SigLIP2, and FineLAP score curves", + "convert normalized squared L2 distance to cosine similarity", + "estimate each modality sample rate from record timestamps", + "retain shorter FineLAP records at audio-window boundaries", + "pass existing timestamped speech spans through unchanged", + "round the paper's floating smoothing width to a sample count", + "extend edge values during moving-average smoothing", + "apply final NMS before fusion because later P2S stages are omitted", + "fuse generated spans with VidXP reciprocal-rank fusion", + ], + }, + "model_calls": 0, + "elapsed_seconds": elapsed_seconds, + "current_control": probe["current_control"], + "adaptation": { + "modalities": modality_reports, + "top_moment_metrics": adapted_metrics, + "result": fused.model_dump(mode="json"), + }, + } + destination = _comparison_path(probe_path, output) + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text( + json.dumps(payload, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + current_result = probe["current_control"]["result"] + current_top = current_result["moments"][0] if current_result["moments"] else None + return { + "output": str(destination), + "model_calls": 0, + "elapsed_seconds": elapsed_seconds, + "expected_interval": payload["expected_interval"], + "current_control": { + "top_interval": ( + { + "start_seconds": current_top["start"], + "end_seconds": current_top["end"], + } + if current_top is not None + else None + ), + "metrics": probe["current_control"]["top_moment_metrics"], + }, + "p2s_asg_adaptation": { + "top_interval": ( + { + "start_seconds": top_moment.start, + "end_seconds": top_moment.end, + } + if top_moment is not None + else None + ), + "metrics": adapted_metrics, + "modality_candidate_counts": { + name: len(report.get("candidates", [])) + for name, report in modality_reports.items() + }, + }, + } + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Compare the saved control with the P2S ASG adaptation." + ) + parser.add_argument("task_id") + parser.add_argument("--probe", type=Path) + parser.add_argument("--output", type=Path) + arguments = parser.parse_args() + if arguments.probe is None: + _load_environment() + probe_path = _output_path(arguments.task_id, None) + else: + probe_path = arguments.probe.resolve() + print(json.dumps(compare_probe(probe_path, arguments.output), indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/condition-state.mjs b/benchmarks/codex-mcp/scripts/condition-state.mjs new file mode 100644 index 00000000..4a2debdc --- /dev/null +++ b/benchmarks/codex-mcp/scripts/condition-state.mjs @@ -0,0 +1,130 @@ +import { spawnSync } from 'node:child_process'; +import { + cpSync, + existsSync, + readFileSync, + rmSync, + writeFileSync, +} from 'node:fs'; +import { + dirname, + isAbsolute, + join, + resolve, +} from 'node:path'; + +export const EVALUATION_PERMISSION_PROFILE = 'vidxp-eval-isolated'; + +function tomlString(value) { + return JSON.stringify(value.replaceAll('\\', '/')); +} + +export function resolveExecutablePath(command, environment = process.env) { + const locator = process.platform === 'win32' ? 'where.exe' : 'which'; + const result = spawnSync(locator, [command], { + env: environment, + encoding: 'utf8', + stdio: 'pipe', + }); + const first = result.stdout?.trim().split(/\r?\n/, 1)[0]; + if (result.status !== 0 || !first || !isAbsolute(first)) { + throw new Error(`${command} must be installed before configuring benchmark isolation.`); + } + return resolve(first); +} + +export function executableInstallRoots(executablePaths) { + return [...new Set(executablePaths.map((path) => dirname(dirname(path))))].sort(); +} + +export function permissionProfile({ networkEnabled, readableRoots = [] }) { + const extraReads = [...new Set(readableRoots)].sort().map( + (path) => `${tomlString(path)} = "read"`, + ); + return [ + `default_permissions = "${EVALUATION_PERMISSION_PROFILE}"`, + '', + `[permissions.${EVALUATION_PERMISSION_PROFILE}.filesystem]`, + '":root" = "deny"', + '":minimal" = "read"', + ...extraReads, + '', + `[permissions.${EVALUATION_PERMISSION_PROFILE}.filesystem.":workspace_roots"]`, + '"." = "write"', + '', + `[permissions.${EVALUATION_PERMISSION_PROFILE}.network]`, + `enabled = ${networkEnabled}`, + '', + ].join('\n'); +} + +function writeIfChanged(path, content) { + if (!existsSync(path) || readFileSync(path, 'utf8') !== content) { + writeFileSync(path, content, 'utf8'); + } +} + +export function evaluationPermissionConfigs(environment = process.env) { + const ffmpeg = resolveExecutablePath('ffmpeg', environment); + const ffprobe = resolveExecutablePath('ffprobe', environment); + const directLocalRoots = executableInstallRoots([ffmpeg, ffprobe]); + return { + ffmpeg, + configs: { + vidxpOn: permissionProfile({ networkEnabled: false }), + vidxpOff: permissionProfile({ + networkEnabled: false, + readableRoots: directLocalRoots, + }), + cleanUser: permissionProfile({ networkEnabled: true }), + }, + }; +} + +export function configureEvaluationIsolation(environment = process.env) { + const requiredHomes = { + vidxpOn: environment.VIDXP_EVAL_VIDXP_ON_CODEX_HOME, + vidxpOff: environment.VIDXP_EVAL_VIDXP_OFF_CODEX_HOME, + cleanUser: environment.VIDXP_EVAL_CLEAN_USER_CODEX_HOME, + }; + for (const [condition, home] of Object.entries(requiredHomes)) { + if (!home || !isAbsolute(home) || !existsSync(home)) { + throw new Error(`The ${condition} Codex home must exist before configuring isolation.`); + } + } + const resolved = evaluationPermissionConfigs(environment); + for (const [condition, home] of Object.entries(requiredHomes)) { + writeIfChanged(join(home, 'config.toml'), resolved.configs[condition]); + } + return resolved; +} + +export function syncEvidenceSkill({ repositoryRoot, environment = process.env }) { + const source = join( + repositoryRoot, + 'plugins', + 'vidxp', + 'skills', + 'vidxp-find-video-evidence', + ); + const destination = join( + environment.VIDXP_EVAL_VIDXP_ON_WORKSPACE || '', + '.agents', + 'skills', + 'vidxp-find-video-evidence', + ); + if (!existsSync(join(source, 'SKILL.md'))) { + throw new Error(`The VidXP evidence skill is missing: ${source}`); + } + if (!isAbsolute(destination)) { + throw new Error('The VidXP-on workspace must exist before syncing the evidence skill.'); + } + rmSync(destination, { recursive: true, force: true }); + cpSync(source, destination, { recursive: true }); +} + +export function prepareConditionState({ repositoryRoot, environment = process.env }) { + const isolation = configureEvaluationIsolation(environment); + syncEvidenceSkill({ repositoryRoot, environment }); + return isolation; +} diff --git a/benchmarks/codex-mcp/scripts/export-eval.mjs b/benchmarks/codex-mcp/scripts/export-eval.mjs new file mode 100644 index 00000000..c6ea89d6 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/export-eval.mjs @@ -0,0 +1,167 @@ +import { spawnSync } from 'node:child_process'; +import { + mkdirSync, + mkdtempSync, + readFileSync, + rmSync, + writeFileSync, +} from 'node:fs'; +import { homedir, tmpdir } from 'node:os'; +import { basename, dirname, join, resolve } from 'node:path'; +import { DatabaseSync } from 'node:sqlite'; +import { fileURLToPath, pathToFileURL } from 'node:url'; + +const scriptDirectory = dirname(fileURLToPath(import.meta.url)); +const benchmarkRoot = resolve(scriptDirectory, '..'); +const repositoryRoot = resolve(benchmarkRoot, '../..'); +const outputDirectory = join(repositoryRoot, 'docs', 'benchmarking', 'runs'); +const promptfooEntrypoint = join( + benchmarkRoot, + 'node_modules', + 'promptfoo', + 'dist', + 'src', + 'entrypoint.js', +); +const SENSITIVE_KEY = /api.?key|access.?token|refresh.?token|secret|password|authorization|cookie/i; + +function replaceAll(value, replacements) { + let result = value; + for (const [source, replacement] of replacements) { + if (source) { + result = result.split(source).join(replacement); + } + } + return result; +} + +function escapeRegExp(value) { + return value.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); +} + +function sanitizeValue(value, replacements, userName) { + if (typeof value === 'string') { + const withPathsReplaced = replaceAll(value, replacements); + const withHomeDirectoriesReplaced = withPathsReplaced + .replace(/\/Users\/[^/\s'"\\]+/g, '') + .replace(/[A-Za-z]:[\\/]Users[\\/][^\\/\s'"\\]+/gi, ''); + return userName + ? withHomeDirectoriesReplaced.replace( + new RegExp(`\\b${escapeRegExp(userName)}\\b`, 'g'), + '', + ) + : withHomeDirectoriesReplaced; + } + if (Array.isArray(value)) { + return value.map((item) => sanitizeValue(item, replacements, userName)); + } + if (!value || typeof value !== 'object') { + return value; + } + const result = {}; + for (const [key, item] of Object.entries(value)) { + if (key === 'sessionId') { + continue; + } + result[key] = SENSITIVE_KEY.test(key) + ? '' + : sanitizeValue(item, replacements, userName); + } + return result; +} + +export function sanitizePromptfooExport( + document, + { + repoRoot = repositoryRoot, + userHome = homedir(), + machineId = process.env.VIDXP_EVAL_MACHINE_ID, + } = {}, +) { + if (!machineId) { + throw new Error('VIDXP_EVAL_MACHINE_ID is required to export a run.'); + } + const copy = structuredClone(document); + for (const result of copy?.results?.results || []) { + if ( + result?.response + && typeof result.response === 'object' + && result.response.metadata?.agentRuntime !== 'local-slm' + ) { + delete result.response.raw; + } + } + const replacements = [ + [repoRoot, ''], + [userHome, ''], + ].sort((left, right) => right[0].length - left[0].length); + const sanitized = sanitizeValue(copy, replacements, basename(userHome)); + sanitized.metadata = { + ...sanitized.metadata, + vidxpExport: { + version: 2, + machineId, + sanitized: true, + omitted: ['Codex raw response bodies', 'session IDs', 'secret values'], + pathPlaceholders: ['', '', ''], + }, + }; + return sanitized; +} + +function latestEvaluationId() { + const configDirectory = process.env.PROMPTFOO_CONFIG_DIR || join(homedir(), '.promptfoo'); + const database = new DatabaseSync(join(configDirectory, 'promptfoo.db'), { readOnly: true }); + try { + const evaluation = database.prepare( + 'SELECT id FROM evals ORDER BY created_at DESC LIMIT 1', + ).get(); + if (!evaluation) { + throw new Error('Promptfoo has no saved evaluation.'); + } + return evaluation.id; + } finally { + database.close(); + } +} + +function artifactName(evaluationId) { + return `${evaluationId.replaceAll(':', '-')}.json`; +} + +function exportEvaluation(evaluationId) { + if (!/^eval-[A-Za-z0-9._:-]+$/.test(evaluationId)) { + throw new Error(`Invalid Promptfoo evaluation ID: ${evaluationId}`); + } + const temporaryDirectory = mkdtempSync(join(tmpdir(), 'vidxp-promptfoo-export-')); + const rawPath = join(temporaryDirectory, 'raw.json'); + try { + const exported = spawnSync( + process.execPath, + [promptfooEntrypoint, 'export', 'eval', evaluationId, '-o', rawPath], + { cwd: benchmarkRoot, env: process.env, stdio: 'inherit' }, + ); + if (exported.status !== 0) { + throw new Error(`Promptfoo export failed for ${evaluationId}.`); + } + const document = JSON.parse(readFileSync(rawPath, 'utf8')); + const sanitized = sanitizePromptfooExport(document); + mkdirSync(outputDirectory, { recursive: true }); + const destination = join(outputDirectory, artifactName(evaluationId)); + writeFileSync(destination, `${JSON.stringify(sanitized, null, 2)}\n`); + process.stdout.write(`Saved sanitized Promptfoo run: ${destination}\n`); + } finally { + rmSync(temporaryDirectory, { recursive: true, force: true }); + } +} + +function main() { + const evaluationIds = process.argv.slice(2); + for (const evaluationId of evaluationIds.length ? evaluationIds : [latestEvaluationId()]) { + exportEvaluation(evaluationId); + } +} + +if (process.argv[1] && import.meta.url === pathToFileURL(process.argv[1]).href) { + main(); +} diff --git a/benchmarks/codex-mcp/scripts/indexing-benchmark.mjs b/benchmarks/codex-mcp/scripts/indexing-benchmark.mjs new file mode 100644 index 00000000..e86a3ceb --- /dev/null +++ b/benchmarks/codex-mcp/scripts/indexing-benchmark.mjs @@ -0,0 +1,305 @@ +import { createHash } from 'node:crypto'; +import { spawnSync } from 'node:child_process'; +import { + existsSync, + mkdirSync, + mkdtempSync, + readFileSync, + readdirSync, + rmSync, + statSync, + writeFileSync, +} from 'node:fs'; +import { tmpdir } from 'node:os'; +import { dirname, join, resolve } from 'node:path'; +import { fileURLToPath } from 'node:url'; + +const benchmarkRoot = resolve(dirname(fileURLToPath(import.meta.url)), '..'); +const repositoryRoot = resolve(benchmarkRoot, '..', '..'); +const manifestPath = join(benchmarkRoot, 'tasks', 'longvale-part9-pilot.json'); +const modalities = ['scene', 'action', 'sound', 'speech']; + +function requireValue(name) { + const value = process.env[name]; + if (!value) { + throw new Error(`${name} is required; run benchmark setup first.`); + } + return value; +} + +function positiveInteger(value) { + if (!/^\d+$/.test(value) || Number(value) < 1) { + throw new Error('Indexing repetitions must be a positive integer.'); + } + return Number(value); +} + +function run(command, args, { capture = false, env = process.env } = {}) { + const result = spawnSync(command, args, { + cwd: repositoryRoot, + env, + encoding: capture ? 'utf8' : undefined, + stdio: capture ? 'pipe' : 'inherit', + }); + if (result.error || result.status !== 0) { + throw new Error( + result.stderr?.trim() + || result.stdout?.trim() + || result.error?.message + || `${command} exited with status ${result.status}.`, + ); + } + return capture ? result.stdout.trim() : ''; +} + +function directorySize(path) { + return readdirSync(path, { withFileTypes: true }).reduce((total, entry) => { + const child = join(path, entry.name); + return total + (entry.isDirectory() ? directorySize(child) : statSync(child).size); + }, 0); +} + +function stats(values) { + const sorted = [...values].sort((left, right) => left - right); + const mean = sorted.reduce((total, value) => total + value, 0) / sorted.length; + const middle = Math.floor(sorted.length / 2); + const median = sorted.length % 2 + ? sorted[middle] + : (sorted[middle - 1] + sorted[middle]) / 2; + const variance = sorted.length < 2 + ? 0 + : sorted.reduce((total, value) => total + (value - mean) ** 2, 0) + / (sorted.length - 1); + return { + mean, + median, + standard_deviation: Math.sqrt(variance), + min: sorted[0], + max: sorted.at(-1), + }; +} + +function secondsSince(started) { + return Number(process.hrtime.bigint() - started) / 1e9; +} + +function sha256(path) { + return createHash('sha256').update(readFileSync(path)).digest('hex'); +} + +function gitRevision() { + try { + return run('git', ['rev-parse', 'HEAD'], { capture: true }); + } catch { + return 'unknown'; + } +} + +function uniqueVideos(tasks, workspace) { + const seen = new Set(); + return tasks.flatMap((task) => { + if (seen.has(task.video_id)) return []; + seen.add(task.video_id); + const mediaPath = join(workspace, task.media_relpath); + if (!existsSync(mediaPath)) { + throw new Error(`Pilot media is missing: ${task.media_relpath}`); + } + return [{ + video_id: task.video_id, + media_path: mediaPath, + duration_seconds: task.duration_seconds, + }]; + }); +} + +function aggregate(repetitions, videos) { + const perVideo = Object.fromEntries(videos.map((video) => { + const measurements = repetitions.flatMap((repetition) => ( + repetition.videos.filter((item) => item.video_id === video.video_id) + )); + return [video.video_id, { + duration_seconds: video.duration_seconds, + import_seconds: stats(measurements.map((item) => item.import_seconds)), + index_seconds: stats(measurements.map((item) => item.index_seconds)), + end_to_end_seconds: stats(measurements.map((item) => item.end_to_end_seconds)), + index_realtime_factor: stats( + measurements.map((item) => item.index_seconds / video.duration_seconds), + ), + }]; + })); + return { + total_elapsed_seconds: stats(repetitions.map((item) => item.total_elapsed_seconds)), + total_import_seconds: stats(repetitions.map((item) => item.total_import_seconds)), + total_index_seconds: stats(repetitions.map((item) => item.total_index_seconds)), + index_size_bytes: stats(repetitions.map((item) => item.index_size_bytes)), + per_video: perVideo, + }; +} + +function main() { + const repetitionsRequested = positiveInteger(process.argv[2] || '3'); + if (process.argv.length > 3) { + throw new Error('Usage: indexing-benchmark.mjs [repetitions]'); + } + const machineId = requireValue('VIDXP_EVAL_MACHINE_ID'); + const workspace = requireValue('VIDXP_EVAL_WORKSPACE'); + const python = requireValue('PROMPTFOO_PYTHON'); + const cli = join(dirname(python), process.platform === 'win32' ? 'vidxp.exe' : 'vidxp'); + if (!existsSync(cli)) { + throw new Error(`The prepared VidXP CLI was not found at ${cli}.`); + } + const tasks = JSON.parse(readFileSync(manifestPath, 'utf8')); + const videos = uniqueVideos(tasks, workspace); + const commandEnvironment = { + ...process.env, + VIDXP_ALLOW_MODEL_DOWNLOADS: 'false', + VIDXP_MODEL_CACHE: requireValue('VIDXP_MODEL_CACHE'), + }; + + const preflight = spawnSync( + process.execPath, + [join(benchmarkRoot, 'scripts', 'preflight.mjs')], + { cwd: benchmarkRoot, env: commandEnvironment, stdio: 'inherit' }, + ); + if (preflight.status !== 0) { + throw new Error('Benchmark preflight failed.'); + } + + const startedAt = new Date(); + const runId = `indexing-${startedAt.toISOString().replaceAll(':', '-').replace(/\.\d{3}Z$/, 'Z')}`; + const outputPath = join(repositoryRoot, 'docs', 'benchmarking', 'runs', `${runId}.json`); + const repetitionResults = []; + process.stdout.write( + `Indexing benchmark on ${machineId}: ${videos.length} videos Ɨ ` + + `${repetitionsRequested} fresh-index repetitions.\n`, + ); + + for (let repetition = 1; repetition <= repetitionsRequested; repetition += 1) { + const root = mkdtempSync(join(tmpdir(), 'vidxp-index-benchmark-')); + const dataDirectory = join(root, 'data'); + const indexDirectory = join(root, 'index'); + mkdirSync(dataDirectory, { recursive: true }); + mkdirSync(indexDirectory, { recursive: true }); + const offset = (repetition - 1) % videos.length; + const ordered = videos.slice(offset).concat(videos.slice(0, offset)); + const measuredVideos = []; + const repetitionStarted = process.hrtime.bigint(); + try { + for (const video of ordered) { + process.stdout.write( + `Repetition ${repetition}/${repetitionsRequested}: ${video.video_id}\n`, + ); + const base = [ + '--data-dir', dataDirectory, + '--index-dir', indexDirectory, + '--device', process.env.VIDXP_EVAL_DEVICE || 'cpu', + '--format', 'json', + ]; + const importStarted = process.hrtime.bigint(); + const imported = JSON.parse(run( + cli, + [...base, 'media', 'import', video.media_path], + { capture: true, env: commandEnvironment }, + )); + const importSeconds = secondsSince(importStarted); + const indexStarted = process.hrtime.bigint(); + run( + cli, + [ + ...base, + 'index', 'create', imported.media_id, + ...modalities.flatMap((modality) => ['--modality', modality]), + ], + { capture: true, env: commandEnvironment }, + ); + const indexSeconds = secondsSince(indexStarted); + measuredVideos.push({ + video_id: video.video_id, + duration_seconds: video.duration_seconds, + import_seconds: importSeconds, + index_seconds: indexSeconds, + end_to_end_seconds: importSeconds + indexSeconds, + index_realtime_factor: indexSeconds / video.duration_seconds, + }); + } + repetitionResults.push({ + repetition, + order: ordered.map((video) => video.video_id), + total_elapsed_seconds: secondsSince(repetitionStarted), + total_import_seconds: measuredVideos.reduce( + (total, video) => total + video.import_seconds, + 0, + ), + total_index_seconds: measuredVideos.reduce( + (total, video) => total + video.index_seconds, + 0, + ), + index_size_bytes: directorySize(indexDirectory), + videos: measuredVideos, + }); + } finally { + rmSync(root, { recursive: true, force: true }); + } + } + + const finishedAt = new Date(); + const summary = aggregate(repetitionResults, videos); + const document = { + schema_version: 1, + run_id: runId, + status: 'complete', + machine_id: machineId, + git_revision: gitRevision(), + started_at: startedAt.toISOString(), + completed_at: finishedAt.toISOString(), + task_manifest_sha256: sha256(manifestPath), + modalities, + repetitions: repetitionsRequested, + media_count: videos.length, + media_duration_seconds: videos.reduce( + (total, video) => total + video.duration_seconds, + 0, + ), + protocol: { + purpose: 'Measure the offline cost paid before the timed agent comparison.', + fresh_data_and_index_per_repetition: true, + prepared_model_cache_reused: true, + model_downloads_allowed: false, + execution: 'Sequential VidXP CLI import and four-modality index per video.', + timing: { + import_seconds: 'Media validation and copy into isolated managed storage.', + index_seconds: 'Blocking four-modality index command, including process and model load.', + total_elapsed_seconds: 'All imports and indexes in one repetition plus loop overhead.', + }, + excluded: ['benchmark setup', 'dataset download', 'model preparation', 'agent inference'], + }, + results: repetitionResults, + aggregate: summary, + }; + mkdirSync(dirname(outputPath), { recursive: true }); + writeFileSync(outputPath, `${JSON.stringify(document, null, 2)}\n`); + console.log('Per-video mean across repetitions:'); + console.table(Object.entries(summary.per_video).map(([videoId, measurement]) => ({ + video: videoId, + 'media seconds': measurement.duration_seconds.toFixed(3), + 'import seconds': measurement.import_seconds.mean.toFixed(3), + 'index seconds': measurement.index_seconds.mean.toFixed(3), + 'combined seconds': measurement.end_to_end_seconds.mean.toFixed(3), + 'index RTF': measurement.index_realtime_factor.mean.toFixed(3), + }))); + console.table([{ + repetitions: repetitionsRequested, + 'mean wall seconds': summary.total_elapsed_seconds.mean.toFixed(3), + 'mean import seconds': summary.total_import_seconds.mean.toFixed(3), + 'mean index seconds': summary.total_index_seconds.mean.toFixed(3), + 'mean index bytes': Math.round(summary.index_size_bytes.mean).toLocaleString('en-US'), + }]); + process.stdout.write(`Saved reviewable indexing run: ${outputPath}\n`); +} + +try { + main(); +} catch (error) { + process.stderr.write(`Indexing benchmark failed: ${error.message}\n`); + process.exitCode = 1; +} diff --git a/benchmarks/codex-mcp/scripts/local_slm_provider.py b/benchmarks/codex-mcp/scripts/local_slm_provider.py new file mode 100644 index 00000000..298324b2 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/local_slm_provider.py @@ -0,0 +1,956 @@ +from __future__ import annotations + +import atexit +import asyncio +import os +import sys +import time +from contextlib import AsyncExitStack +from pathlib import Path +from typing import Any, Literal +from urllib.parse import urlsplit, urlunsplit +from uuid import uuid4 + +import httpx +from mcp import ClientSession +from mcp.client.stdio import StdioServerParameters, stdio_client +from pydantic import BaseModel, Field, field_validator +from pydantic_ai import Agent, ModelProfile, NativeOutput +from pydantic_ai.models.test import TestModel +from pydantic_ai.usage import RunUsage, UsageLimits + +from vidxp.application_models import Job, JobState, JobWaitResult, MediaPage +from vidxp.benchmarks.agent_ablation_score import ( + DEFAULT_MAX_CANDIDATES, + DEFAULT_TARGET_CHUNK_SECONDS, + bounded_chunk_window, +) +from vidxp.infrastructure.ollama_query import ( + create_local_answer_model, + local_answer_model_settings, +) +from vidxp.local_answers import ( + LocalAnswerConfiguration, + LocalAnswerError, + ManagedOllamaSession, + inspect_local_answers, + load_local_answer_configuration, + local_answer_spec, +) + +from modality_probe import BENCHMARK_ROOT, _load_environment, _required_environment + + +REPOSITORY_ROOT = BENCHMARK_ROOT.parent.parent +ALLOWED_TOOLS = frozenset( + { + "list_media", + "search_moments", + "wait_job", + "get_job_evidence", + } +) +ROUTER_PROMPT = BENCHMARK_ROOT / "prompts" / "local-slm-router.txt" +PLANNER_PROMPT = BENCHMARK_ROOT / "prompts" / "local-slm-planner.txt" + +_runtime: ManagedOllamaSession | None = None + + +class Candidate(BaseModel): + start_seconds: float + end_seconds: float + modalities: list[str] + description: str + evidence_ids: list[str] + + +class LocalAgentAnswer(BaseModel): + video_id: str + answer: str + source_job_id: str | None + candidates: list[Candidate] = Field(max_length=DEFAULT_MAX_CANDIDATES) + + +class ModalityRoute(BaseModel): + modalities: list[Literal["scene", "action", "sound", "speech"]] = Field( + min_length=1, + max_length=4, + ) + + @field_validator("modalities") + @classmethod + def _unique_modalities(cls, values: list[str]) -> list[str]: + if len(values) != len(set(values)): + raise ValueError("Routing modalities must be unique.") + return values + + +class SearchPlan(ModalityRoute): + query: str + candidate_top_k: int + + @field_validator("query") + @classmethod + def _valid_query(cls, value: str) -> str: + query = value.strip() + if not query or len(query) > 4096: + raise ValueError("The planned search query must contain 1ā€“4096 characters.") + return query + + @field_validator("candidate_top_k") + @classmethod + def _valid_candidate_depth(cls, value: int) -> int: + if isinstance(value, bool) or not DEFAULT_MAX_CANDIDATES <= value <= 100: + raise ValueError("The planned candidate depth must be from 3 through 100.") + return value + + +# Promptfoo imports providers without registering the module in sys.modules. +# Resolve postponed field annotations while this module namespace is available. +LocalAgentAnswer.model_rebuild(_types_namespace=globals()) +ModalityRoute.model_rebuild(_types_namespace=globals()) +SearchPlan.model_rebuild(_types_namespace=globals()) + + +class StdioMCPClient: + """Call the fixed VidXP retrieval workflow and retain an auditable trace.""" + + def __init__(self, parameters: StdioServerParameters) -> None: + self.parameters = parameters + self.session: ClientSession | None = None + self._transport: AsyncExitStack | None = None + self.calls: list[dict[str, Any]] = [] + + async def open(self) -> None: + if self._transport is not None: + return + transport = AsyncExitStack() + try: + read_stream, write_stream = await transport.enter_async_context( + stdio_client(self.parameters) + ) + session = await transport.enter_async_context( + ClientSession(read_stream, write_stream) + ) + await session.initialize() + except BaseException: + await transport.aclose() + raise + self._transport = transport + self.session = session + + async def close(self) -> None: + transport = self._transport + self._transport = None + self.session = None + if transport is not None: + await transport.aclose() + + async def call(self, name: str, arguments: dict[str, Any]) -> dict[str, Any]: + if name not in ALLOWED_TOOLS: + raise RuntimeError(f"The local-SLM harness cannot call MCP tool {name}.") + if self.session is None: + raise RuntimeError("The VidXP MCP session is not initialized.") + started_at = time.time() + started = time.perf_counter() + call = { + "name": name, + "arguments": arguments, + "started_at": started_at, + } + try: + result = await self.session.call_tool(name, arguments=arguments) + except Exception as error: + self.calls.append( + { + **call, + "elapsed_seconds": time.perf_counter() - started, + "is_error": True, + "error_type": type(error).__name__, + "error_message": str(error)[:1000], + } + ) + raise + messages = [ + item.text + for item in result.content + if getattr(item, "type", None) == "text" + ] + recorded_call = { + **call, + "elapsed_seconds": time.perf_counter() - started, + "is_error": result.is_error, + } + if result.is_error: + recorded_call["error_message"] = ( + "; ".join(messages) or f"VidXP tool {name} failed." + )[:1000] + self.calls.append(recorded_call) + if result.is_error: + raise RuntimeError("; ".join(messages) or f"VidXP tool {name} failed.") + if not isinstance(result.structured_content, dict): + raise RuntimeError(f"VidXP tool {name} returned no structured result.") + return result.structured_content + + +def _positive_option(config: dict[str, Any], name: str) -> int: + value = config.get(name) + if isinstance(value, bool) or not isinstance(value, int) or value < 1: + raise ValueError(f"The local-SLM provider requires a positive {name} value.") + return value + + +def _positive_number_option(config: dict[str, Any], name: str) -> float: + value = config.get(name) + if isinstance(value, bool) or not isinstance(value, (int, float)) or value <= 0: + raise ValueError(f"The local-SLM provider requires a positive {name} value.") + return float(value) + + +def _strategy(config: dict[str, Any]) -> Literal["router", "planner"]: + value = config.get("strategy") + if value not in {"router", "planner"}: + raise ValueError("The local-SLM provider strategy must be router or planner.") + return value + + +def _instructions(path: Path) -> str: + try: + instructions = path.read_text(encoding="utf-8").strip() + except OSError as error: + raise RuntimeError(f"The local-SLM prompt is unavailable: {path.name}.") from error + if not instructions: + raise RuntimeError(f"The local-SLM prompt is empty: {path.name}.") + return instructions + + +def _selected_model() -> tuple[LocalAnswerConfiguration, str, str]: + try: + configured = load_local_answer_configuration() + except (OSError, ValueError) as error: + raise RuntimeError( + "The saved local-answer configuration is invalid. Rerun " + "`uv run --no-sync vidxp local-answers prepare --yes`." + ) from error + if configured is None: + raise RuntimeError( + "No saved local-answer runtime exists. Run `uv run --no-sync vidxp " + "local-answers prepare --yes`." + ) + base_url = os.environ.get("VIDXP_SLM_BASE_URL") or configured.base_url + model_name = os.environ.get("VIDXP_SLM_MODEL") or configured.model + parsed = urlsplit(base_url) + if parsed.hostname not in {"127.0.0.1", "localhost", "::1"}: + raise RuntimeError("The local-SLM benchmark requires a loopback Ollama endpoint.") + return configured, base_url, model_name + + +def _ensure_runtime( + configured: LocalAnswerConfiguration, + *, + base_url: str, + model_name: str, + context_tokens: int, +) -> bool: + global _runtime + if _runtime is None: + _runtime = ManagedOllamaSession( + configured.model_copy(update={"base_url": base_url, "model": model_name}), + context_tokens=context_tokens, + ) + _runtime.ensure_started() + return True + _runtime.ensure_started() + return False + + +def _close_runtime() -> None: + global _runtime + if _runtime is not None: + _runtime.close() + _runtime = None + + +atexit.register(_close_runtime) + + +def _require_local_model( + configured: LocalAnswerConfiguration, + *, + base_url: str, + model_name: str, +) -> dict[str, str]: + status = inspect_local_answers( + base_url=base_url, + model=model_name, + configuration=configured, + ) + if not status.ready: + raise RuntimeError("; ".join(status.errors) or "The local model is not ready.") + return {"provider": "ollama", "model": model_name, "endpoint_scope": "loopback"} + + +def _mcp_parameters() -> StdioServerParameters: + environment = { + **os.environ, + "VIDXP_ALLOW_MODEL_DOWNLOADS": "false", + "VIDXP_MODEL_CACHE": _required_environment("VIDXP_MODEL_CACHE"), + # The benchmarked local model is the agent. Prevent query_video from + # silently starting a second, unmetered model inside the MCP process. + "VIDXP_SLM_BASE_URL": "", + "VIDXP_SLM_MODEL": "", + } + return StdioServerParameters( + command=_required_environment("VIDXP_MCP_COMMAND"), + args=[ + "--repository", + os.environ.get("VIDXP_EVAL_REPOSITORY", "default"), + "--index-directory", + _required_environment("VIDXP_EVAL_INDEX_DIR"), + "--data-dir", + _required_environment("VIDXP_EVAL_DATA_DIR"), + "--device", + os.environ.get("VIDXP_EVAL_DEVICE", "cpu"), + ], + env=environment, + cwd=REPOSITORY_ROOT, + ) + + +def _trace(calls: list[dict[str, Any]], started_at: float) -> dict[str, Any]: + return { + "spans": [ + { + "name": f"mcp__vidxp__{call['name']}", + "start_time": call.get("started_at", started_at), + "attributes": { + "codex.mcp.server": "vidxp", + "codex.mcp.tool": call["name"], + "codex.mcp.input": call["arguments"], + }, + } + for call in calls + ] + } + + +def _activity(calls: list[dict[str, Any]]) -> dict[str, Any]: + return { + "items": [ + { + "type": "mcp_tool_call", + "server": "vidxp", + "tool": call["name"], + "arguments": call["arguments"], + "elapsed_seconds": call["elapsed_seconds"], + "is_error": call["is_error"], + **( + {"error_type": call["error_type"]} + if "error_type" in call + else {} + ), + **( + {"error_message": call["error_message"]} + if "error_message" in call + else {} + ), + } + for call in calls + ] + } + + +def _metadata( + *, + calls: list[dict[str, Any]], + started_at: float, + model_identity: dict[str, str], + model_settings: dict[str, Any], + agent_role: str, + cold_start: bool, + selected_modalities: list[str] | None = None, + search_query: str | None = None, + candidate_top_k: int | None = None, + instruction_profile: str, + model_turns: int | None = None, +) -> dict[str, Any]: + return { + "agentRuntime": "local-slm", + "agentRole": agent_role, + "model": model_identity, + "modelSettings": model_settings, + "modelTurns": model_turns, + "coldStart": cold_start, + "instructionProfile": instruction_profile, + "selectedModalities": selected_modalities, + "searchQuery": search_query, + "candidateTopK": candidate_top_k, + "trace": _trace(calls, started_at), + "skillCalls": [], + } + + +def _token_usage(usage: RunUsage) -> dict[str, Any]: + token_usage: dict[str, Any] = { + "prompt": usage.input_tokens, + "completion": usage.output_tokens, + "total": usage.input_tokens + usage.output_tokens, + "cached": usage.cache_read_tokens, + "numRequests": usage.requests, + } + reasoning_tokens = usage.details.get("reasoning_tokens") + if isinstance(reasoning_tokens, int): + token_usage["completionDetails"] = {"reasoning": reasoning_tokens} + return token_usage + + +async def _loaded_context_tokens(*, base_url: str, model_name: str) -> int | None: + """Read the context Ollama actually allocated to the loaded benchmark model.""" + + parsed = urlsplit(base_url) + management_root = urlunsplit((parsed.scheme, parsed.netloc, "", "", "")) + try: + async with httpx.AsyncClient(timeout=5) as client: + response = await client.get(f"{management_root}/api/ps") + response.raise_for_status() + payload = response.json() + except (httpx.HTTPError, ValueError): + return None + if not isinstance(payload, dict): + return None + models = payload.get("models", []) + if not isinstance(models, list): + return None + for model in models: + if not isinstance(model, dict): + continue + if model.get("name") not in {model_name, f"{model_name}:latest"} and model.get( + "model" + ) not in {model_name, f"{model_name}:latest"}: + continue + context_length = model.get("context_length") + if isinstance(context_length, int) and context_length > 0: + return context_length + return None + + +async def _record_actual_context( + *, + base_url: str, + model_name: str, + requested_context_tokens: int, + model_settings: dict[str, Any], +) -> dict[str, Any]: + actual = await _loaded_context_tokens(base_url=base_url, model_name=model_name) + recorded = {**model_settings, "loadedContextTokens": actual} + if actual is not None and actual < requested_context_tokens: + raise RuntimeError( + "Ollama loaded the benchmark model with " + f"{actual} context tokens, below the required " + f"{requested_context_tokens}. Stop the existing Ollama service so the " + "saved VidXP runtime can start with the benchmark configuration." + ) + return recorded + + +def _build_router( + *, + base_url: str, + model_name: str, + max_output_tokens: int, + model_timeout_seconds: float, +) -> Agent[None, ModalityRoute]: + model = create_local_answer_model( + base_url=base_url, + model_name=model_name, + ) + + settings = dict( + local_answer_model_settings( + max_tokens=max_output_tokens, + timeout_seconds=model_timeout_seconds, + ) + ) + return Agent( + model, + output_type=NativeOutput(ModalityRoute), + instructions=_instructions(ROUTER_PROMPT), + retries=0, + model_settings=settings, + ) + + +def _build_planner( + *, + base_url: str, + model_name: str, + max_output_tokens: int, + model_timeout_seconds: float, +) -> Agent[None, SearchPlan]: + model = create_local_answer_model( + base_url=base_url, + model_name=model_name, + ) + settings = dict( + local_answer_model_settings( + max_tokens=max_output_tokens, + timeout_seconds=model_timeout_seconds, + ) + ) + return Agent( + model, + output_type=NativeOutput(SearchPlan), + instructions=_instructions(PLANNER_PROMPT), + retries=0, + model_settings=settings, + ) + + +def _benchmark_inputs( + context: dict[str, Any] | None, +) -> tuple[str, str, str, float]: + variables = (context or {}).get("vars") + if not isinstance(variables, dict): + raise ValueError("Promptfoo did not provide structured benchmark variables.") + video_id = variables.get("video_id") + query = variables.get("query") + media_relpath = variables.get("media_relpath") + target_chunk_seconds = variables.get( + "target_chunk_seconds", + DEFAULT_TARGET_CHUNK_SECONDS, + ) + if not isinstance(video_id, str) or not video_id.strip(): + raise ValueError("The local-SLM case requires a video_id variable.") + if not isinstance(query, str) or not query.strip(): + raise ValueError("The local-SLM case requires a query variable.") + if not isinstance(media_relpath, str) or not media_relpath.strip(): + raise ValueError("The local-SLM case requires a media_relpath variable.") + if ( + isinstance(target_chunk_seconds, bool) + or not isinstance(target_chunk_seconds, (int, float)) + or target_chunk_seconds <= 0 + ): + raise ValueError("The local-SLM case requires a positive target chunk size.") + filename = media_relpath.replace("\\", "/").rsplit("/", 1)[-1] + if not filename: + raise ValueError("The local-SLM media_relpath has no filename.") + return video_id.strip(), query.strip(), filename, float(target_chunk_seconds) + + +async def _retrieve_evidence( + *, + client: StdioMCPClient, + video_id: str, + filename: str, + query: str, + modalities: list[str], + target_chunk_seconds: float, + candidate_top_k: int | None, +) -> LocalAgentAnswer: + media_page = MediaPage.model_validate( + await client.call("list_media", {"filename": filename}) + ) + exact_matches = [ + item + for item in media_page.items + if item.original_filename == filename + ] + if len(exact_matches) != 1: + raise RuntimeError( + f"Expected one registered VidXP video named {filename}, found " + f"{len(exact_matches)}." + ) + media_id = exact_matches[0].media_id + media_duration = exact_matches[0].duration_seconds + if media_duration is None: + raise RuntimeError(f"Registered video {filename} has no probed duration.") + + search_command: dict[str, Any] = { + "media_id": media_id, + "query": query, + "modalities": modalities, + "top_k": DEFAULT_MAX_CANDIDATES, + } + if candidate_top_k is not None: + search_command["candidate_top_k"] = candidate_top_k + submitted = Job.model_validate( + await client.call( + "search_moments", + { + "command": search_command, + "idempotency_key": f"local-slm-{uuid4().hex}", + }, + ) + ) + job_id = submitted.job_id + terminal = submitted.terminal + state = submitted.state + error = submitted.error + + observation_token: str | None = None + while not terminal: + wait_arguments: dict[str, Any] = { + "job_id": job_id, + "timeout_seconds": 30, + } + if observation_token is not None: + wait_arguments["after_observation_token"] = observation_token + waited = JobWaitResult.model_validate( + await client.call("wait_job", wait_arguments) + ) + terminal = waited.job.terminal + state = waited.job.state + error = waited.job.error + observation_token = waited.job.observation_token + + if state != JobState.succeeded: + raise RuntimeError( + f"VidXP search job {job_id} ended in state {state.value}: " + f"{error.message if error is not None else 'no error detail'}" + ) + + evidence = await client.call("get_job_evidence", {"job_id": job_id}) + board = evidence.get("board") + if not isinstance(board, dict): + raise RuntimeError("VidXP returned an invalid evidence board result.") + tiles = board.get("tiles") + if not isinstance(tiles, list): + raise RuntimeError("VidXP evidence board returned an invalid tile collection.") + + candidates: list[Candidate] = [] + seen_evidence: set[str] = set() + seen_intervals: set[tuple[float, float]] = set() + ready_tiles = sorted( + (tile for tile in tiles if isinstance(tile, dict) and tile.get("state") == "ready"), + key=lambda tile: tile.get("rank", 10**9), + ) + for tile in ready_tiles: + evidence_id = tile.get("evidence_id") + if not isinstance(evidence_id, str) or evidence_id in seen_evidence: + continue + tile_modalities = tile.get("modalities") + if not isinstance(tile_modalities, list) or not all( + isinstance(modality, str) for modality in tile_modalities + ): + raise RuntimeError("VidXP evidence tile returned invalid modalities.") + description = tile.get("display_text") + rank = tile.get("rank") + clip_start, clip_end = bounded_chunk_window( + tile.get("start"), + tile.get("end"), + media_duration=media_duration, + target_chunk_seconds=target_chunk_seconds, + ) + interval = (clip_start, clip_end) + if interval in seen_intervals: + continue + candidates.append( + Candidate( + start_seconds=clip_start, + end_seconds=clip_end, + modalities=tile_modalities, + description=( + description + if isinstance(description, str) and description + else f"VidXP ranked evidence candidate {rank}." + ), + evidence_ids=[evidence_id], + ) + ) + seen_evidence.add(evidence_id) + seen_intervals.add(interval) + if len(candidates) == DEFAULT_MAX_CANDIDATES: + break + + return LocalAgentAnswer( + video_id=video_id, + answer=( + f"VidXP returned {len(candidates)} ranked evidence candidate" + f"{'s' if len(candidates) != 1 else ''}." + if candidates + else "VidXP returned no ready evidence candidates." + ), + source_job_id=job_id, + candidates=candidates, + ) + + +async def _run_provider( + *, + config: dict[str, Any], + context: dict[str, Any] | None, +) -> dict[str, Any]: + video_id, query, filename, target_chunk_seconds = _benchmark_inputs(context) + configured, base_url, model_name = _selected_model() + context_tokens = _positive_option(config, "contextTokens") + max_output_tokens = _positive_option(config, "maxOutputTokens") + strategy = _strategy(config) + instruction_profile = ( + "targeted-modality-router-v1" + if strategy == "router" + else "targeted-search-planner-v1" + ) + agent_role = "modality-router" if strategy == "router" else "search-planner" + defaults = local_answer_spec().defaults + recorded_model_settings = { + "contextTokens": context_tokens, + "maxOutputTokens": max_output_tokens, + "reasoningEffort": "none", + "temperature": defaults.temperature, + "topP": defaults.top_p, + "presencePenalty": defaults.presence_penalty, + "role": ( + "modality-selection-only" + if strategy == "router" + else "search-planning-only" + ), + } + cold_start = _ensure_runtime( + configured, + base_url=base_url, + model_name=model_name, + context_tokens=context_tokens, + ) + model_identity = _require_local_model( + configured, + base_url=base_url, + model_name=model_name, + ) + started_at = time.time() + usage = RunUsage() + selected_modalities: list[str] | None = None + search_query: str | None = None + candidate_top_k: int | None = None + client = StdioMCPClient(_mcp_parameters()) + try: + builder = _build_router if strategy == "router" else _build_planner + agent = builder( + base_url=base_url, + model_name=model_name, + max_output_tokens=max_output_tokens, + model_timeout_seconds=_positive_number_option( + config, + "modelTimeoutSeconds", + ), + ) + planned = await agent.run( + query, + usage_limits=UsageLimits(request_limit=1), + usage=usage, + ) + selected_modalities = list(planned.output.modalities) + search_query = query + if isinstance(planned.output, SearchPlan): + search_query = planned.output.query + candidate_top_k = planned.output.candidate_top_k + await client.open() + answer = await _retrieve_evidence( + client=client, + video_id=video_id, + filename=filename, + query=search_query, + modalities=selected_modalities, + target_chunk_seconds=target_chunk_seconds, + candidate_top_k=candidate_top_k, + ) + except Exception as error: + recorded_model_settings = await _record_actual_context( + base_url=base_url, + model_name=model_name, + requested_context_tokens=context_tokens, + model_settings=recorded_model_settings, + ) + return { + "error": f"{type(error).__name__}: {error}", + "cost": 0, + "tokenUsage": _token_usage(usage), + "metadata": _metadata( + calls=client.calls, + started_at=started_at, + model_identity=model_identity, + model_settings=recorded_model_settings, + agent_role=agent_role, + cold_start=cold_start, + selected_modalities=selected_modalities, + search_query=search_query, + candidate_top_k=candidate_top_k, + instruction_profile=instruction_profile, + model_turns=usage.requests, + ), + "raw": { + **_activity(client.calls), + "usage": { + "requests": usage.requests, + "tool_calls": len(client.calls), + }, + }, + } + finally: + await client.close() + + recorded_model_settings = await _record_actual_context( + base_url=base_url, + model_name=model_name, + requested_context_tokens=context_tokens, + model_settings=recorded_model_settings, + ) + calls = client.calls + return { + "output": answer.model_dump_json(), + "format": "json", + "cost": 0, + "tokenUsage": _token_usage(usage), + "metadata": _metadata( + calls=calls, + started_at=started_at, + model_identity=model_identity, + model_settings=recorded_model_settings, + agent_role=agent_role, + cold_start=cold_start, + selected_modalities=selected_modalities, + search_query=search_query, + candidate_top_k=candidate_top_k, + instruction_profile=instruction_profile, + model_turns=usage.requests, + ), + "raw": { + **_activity(calls), + "usage": { + "requests": usage.requests, + "tool_calls": len(calls), + }, + }, + } + + +def call_api( + prompt: str, + options: dict[str, Any] | None = None, + context: dict[str, Any] | None = None, +) -> dict[str, Any]: + """Run one local-SLM VidXP case through Promptfoo.""" + + del prompt + _load_environment() + config = (options or {}).get("config", {}) + try: + return asyncio.run(_run_provider(config=config, context=context)) + except (LocalAnswerError, RuntimeError, ValueError) as error: + return {"error": str(error)} + + +async def _check_provider_wiring(*, base_url: str, model_name: str) -> int: + """Exercise router schemas and MCP discovery without real model inference.""" + + client = StdioMCPClient(_mcp_parameters()) + await client.open() + try: + if client.session is None: + raise RuntimeError("The VidXP MCP session is not initialized.") + response = await client.session.list_tools() + tools = {tool.name for tool in response.tools} + missing = ALLOWED_TOOLS.difference(tools) + if missing: + raise RuntimeError( + "The local-SLM harness is missing VidXP MCP tools: " + f"{', '.join(sorted(missing))}." + ) + router = _build_router( + base_url=base_url, + model_name=model_name, + max_output_tokens=1, + model_timeout_seconds=1, + ) + if router.model.profile.get("openai_chat_supports_max_completion_tokens"): + raise RuntimeError( + "The local-SLM agent is not using Ollama's max_tokens request field." + ) + test_model = TestModel( + custom_output_text=ModalityRoute( + modalities=["scene"] + ).model_dump_json(), + profile=ModelProfile(supports_json_schema_output=True), + ) + result = await router.run("A visible landscape.", model=test_model) + if result.usage.requests != 1: + raise RuntimeError("The local-SLM usage contract is unavailable.") + parameters = test_model.last_model_request_parameters + if parameters is None or parameters.function_tools: + raise RuntimeError("The local-SLM router unexpectedly exposes tools.") + if result.output.modalities != ["scene"] or client.calls: + raise RuntimeError("The local-SLM no-inference wiring check was not isolated.") + planner = _build_planner( + base_url=base_url, + model_name=model_name, + max_output_tokens=1, + model_timeout_seconds=1, + ) + planned = await planner.run( + "A visible landscape.", + model=TestModel( + custom_output_text=SearchPlan( + query="visible landscape", + modalities=["scene"], + candidate_top_k=20, + ).model_dump_json(), + profile=ModelProfile(supports_json_schema_output=True), + ), + ) + if ( + planned.output.query != "visible landscape" + or planned.output.modalities != ["scene"] + or planned.output.candidate_top_k != 20 + or client.calls + ): + raise RuntimeError("The local-SLM planner wiring check was not isolated.") + return len(ALLOWED_TOOLS) + finally: + await client.close() + + +def check_configuration() -> dict[str, Any]: + """Validate the prepared runtime and agent wiring without model inference.""" + + _load_environment() + configured, base_url, model_name = _selected_model() + if configured.executable is None or not configured.executable.is_file(): + raise RuntimeError("The saved local-answer executable is missing.") + if configured.model_directory is None or not configured.model_directory.is_dir(): + raise RuntimeError("The saved local-answer model directory is missing.") + for name in ( + "VIDXP_EVAL_DATA_DIR", + "VIDXP_EVAL_INDEX_DIR", + "VIDXP_MODEL_CACHE", + "VIDXP_MCP_COMMAND", + "VIDXP_EVAL_MACHINE_ID", + ): + _required_environment(name) + ModalityRoute.model_json_schema() + SearchPlan.model_json_schema() + LocalAgentAnswer.model_json_schema() + tool_count = asyncio.run( + _check_provider_wiring(base_url=base_url, model_name=model_name) + ) + return { + "model": model_name, + "base_url": base_url, + "runtime": str(configured.executable), + "model_directory": str(configured.model_directory), + "mcp_tools": tool_count, + } + + +if __name__ == "__main__": + if sys.argv[1:] != ["--check"]: + raise SystemExit("Usage: local_slm_provider.py --check") + try: + checked = check_configuration() + print( + "Local SLM planning ready without inference: " + f"{checked['model']}, separate router and planner prompts, " + "one structured decision per condition, " + f"{checked['mcp_tools']} harness-owned MCP tools, and deterministic " + "evidence output verified." + ) + except (RuntimeError, ValueError) as error: + raise SystemExit(str(error)) from error diff --git a/benchmarks/codex-mcp/scripts/mcp_preflight.py b/benchmarks/codex-mcp/scripts/mcp_preflight.py new file mode 100644 index 00000000..77e7145f --- /dev/null +++ b/benchmarks/codex-mcp/scripts/mcp_preflight.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +import asyncio +import json +import os +from pathlib import Path +from typing import Any + +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + + +BENCHMARK_ROOT = Path(__file__).resolve().parent.parent +MANIFEST_PATH = BENCHMARK_ROOT / "tasks" / "longvale-part9-pilot.json" +REQUIRED_MODALITIES = frozenset({"scene", "action", "sound", "speech"}) +REQUIRED_TOOLS = frozenset( + {"get_runtime_readiness", "get_workspace", "search_moments", "wait_job", "get_job_evidence"} +) + + +def _required_environment(name: str) -> str: + value = os.environ.get(name) + if not value: + raise RuntimeError(f"{name} is required for MCP preflight.") + return value + + +def _structured(result: Any, tool: str) -> dict[str, Any]: + if getattr(result, "is_error", False): + raise RuntimeError(f"{tool} returned an MCP error.") + content = getattr(result, "structured_content", None) + if not isinstance(content, dict): + raise RuntimeError(f"{tool} did not return structured content.") + return content + + +async def _preflight() -> None: + tasks = json.loads(MANIFEST_PATH.read_text(encoding="utf-8")) + filenames = {Path(task["media_relpath"]).name for task in tasks} + expected_durations = { + Path(task["media_relpath"]).name: float(task["duration_seconds"]) + for task in tasks + } + server_environment = dict(os.environ) + server_environment["VIDXP_MODEL_CACHE"] = _required_environment( + "VIDXP_MODEL_CACHE" + ) + server_environment["VIDXP_ALLOW_MODEL_DOWNLOADS"] = "false" + parameters = StdioServerParameters( + command=_required_environment("VIDXP_MCP_COMMAND"), + args=[ + "--repository", + os.environ.get("VIDXP_EVAL_REPOSITORY", "default"), + "--index-directory", + _required_environment("VIDXP_EVAL_INDEX_DIR"), + "--data-dir", + _required_environment("VIDXP_EVAL_DATA_DIR"), + "--device", + os.environ.get("VIDXP_EVAL_DEVICE", "cpu"), + ], + env=server_environment, + ) + async with stdio_client(parameters) as (read_stream, write_stream): + async with ClientSession(read_stream, write_stream) as session: + await session.initialize() + tools = {tool.name for tool in (await session.list_tools()).tools} + missing_tools = sorted(REQUIRED_TOOLS - tools) + if missing_tools: + raise RuntimeError( + f"VidXP MCP is missing required tools: {', '.join(missing_tools)}" + ) + + readiness = _structured( + await session.call_tool("get_runtime_readiness"), + "get_runtime_readiness", + ) + dependencies = readiness.get("dependencies") + checks = dependencies.get("checks") if isinstance(dependencies, dict) else None + if not isinstance(checks, list): + raise RuntimeError("Runtime readiness did not report model checks.") + failed_models = sorted( + str(check.get("capability")) + for check in checks + if isinstance(check, dict) + and check.get("kind") == "model" + and str(check.get("capability", "")).split(".", 1)[0] + in REQUIRED_MODALITIES + and check.get("ok") is not True + ) + covered_modalities = { + str(check.get("capability", "")).split(".", 1)[0] + for check in checks + if isinstance(check, dict) and check.get("kind") == "model" + } + missing_model_checks = sorted(REQUIRED_MODALITIES - covered_modalities) + if failed_models or missing_model_checks: + details = [ + *(f"unready: {name}" for name in failed_models), + *(f"unchecked: {name}" for name in missing_model_checks), + ] + raise RuntimeError( + "Required MCP models are not ready in VIDXP_MODEL_CACHE (" + + ", ".join(details) + + ")." + ) + + workspace = _structured( + await session.call_tool("get_workspace", {"page_size": 100}), + "get_workspace", + ) + capability_readiness = { + item.get("name"): item.get("models_ready") + for item in workspace.get("capabilities", []) + if isinstance(item, dict) + } + unavailable = sorted( + modality + for modality in REQUIRED_MODALITIES + if capability_readiness.get(modality) is not True + ) + if unavailable: + raise RuntimeError( + "MCP workspace reports unready models for: " + + ", ".join(unavailable) + ) + + media = { + item.get("original_filename"): item + for item in workspace.get("media", []) + if isinstance(item, dict) + } + missing_media = sorted(filenames - media.keys()) + if missing_media: + raise RuntimeError( + "MCP workspace is missing pilot media: " + + ", ".join(missing_media) + ) + for filename in sorted(filenames): + item = media[filename] + duration = item.get("duration_seconds") + if ( + not isinstance(duration, (int, float)) + or abs(float(duration) - expected_durations[filename]) > 0.001 + ): + raise RuntimeError( + f"Pilot manifest duration does not match {filename}." + ) + indexed = { + capability.get("name") + for capability in item.get("capabilities", []) + if isinstance(capability, dict) and capability.get("indexed") is True + } + if ( + item.get("state") != "ready" + or item.get("in_active_snapshot") is not True + or not REQUIRED_MODALITIES.issubset(indexed) + ): + raise RuntimeError( + f"MCP workspace is not fully indexed for {filename}." + ) + + print( + "VidXP MCP ready: exact server environment, required models, " + f"{len(filenames)} indexed pilot videos, and evidence tools verified." + ) + + +if __name__ == "__main__": + asyncio.run(_preflight()) diff --git a/benchmarks/codex-mcp/scripts/modality_probe.py b/benchmarks/codex-mcp/scripts/modality_probe.py new file mode 100644 index 00000000..f6d8ddcf --- /dev/null +++ b/benchmarks/codex-mcp/scripts/modality_probe.py @@ -0,0 +1,343 @@ +from __future__ import annotations + +import argparse +import json +import os +import shlex +import time +from collections.abc import Callable +from pathlib import Path +from typing import Any, Mapping + +from vidxp.application_models import ListMediaCommand, MediaState, SearchResult +from vidxp.benchmarks.agent_ablation_score import interval_iou +from vidxp.capabilities.action.operations import search_videoprism +from vidxp.capabilities.action.specs import VIDEOPRISM_MODEL +from vidxp.capabilities.scene.operations import search_scene +from vidxp.capabilities.scene.specs import SIGLIP2_MODEL +from vidxp.capabilities.sound.operations import search_sound +from vidxp.capabilities.sound.specs import FINELAP_MODEL +from vidxp.capabilities.speech.operations import search_speech +from vidxp.capabilities.speech.specs import QWEN3_EMBEDDING_MODEL +from vidxp.composition import create_local_application +from vidxp.core.contracts import IndexConfig +from vidxp.ports import IndexStore, ModelRuntimePort +from vidxp.search_fusion import fuse_search_results + + +BENCHMARK_ROOT = Path(__file__).resolve().parent.parent +TASKS_PATH = BENCHMARK_ROOT / "tasks" / "longvale-part9-pilot.json" +SearchFunction = Callable[..., SearchResult] +SEARCHERS: dict[str, SearchFunction] = { + "action": search_videoprism, + "scene": search_scene, + "sound": search_sound, + "speech": search_speech, +} +MODELS = { + "action": VIDEOPRISM_MODEL, + "scene": SIGLIP2_MODEL, + "sound": FINELAP_MODEL, + "speech": QWEN3_EMBEDDING_MODEL, +} + + +def _load_environment() -> None: + path = BENCHMARK_ROOT / ".env" + if not path.is_file(): + raise RuntimeError("run benchmark setup before probing indexed evidence") + for raw_line in path.read_text(encoding="utf-8").splitlines(): + line = raw_line.strip() + if not line or line.startswith("#") or "=" not in line: + continue + name, raw_value = line.split("=", 1) + values = shlex.split(raw_value, posix=True) + if len(values) != 1: + raise RuntimeError(f"invalid value for {name} in benchmark .env") + os.environ.setdefault(name, values[0]) + + +def _task(task_id: str) -> dict[str, Any]: + tasks = json.loads(TASKS_PATH.read_text(encoding="utf-8")) + matches = [task for task in tasks if task.get("id") == task_id] + if len(matches) != 1: + raise ValueError(f"unknown task id: {task_id}") + return matches[0] + + +def _required_environment(name: str) -> str: + value = os.environ.get(name) + if not value: + raise RuntimeError(f"{name} is missing from benchmark .env") + return value + + +def _output_path(task_id: str, requested: Path | None) -> Path: + if requested is not None: + return requested.resolve() + data_directory = Path(_required_environment("VIDXP_EVAL_DATA_DIR")) + return data_directory.parent / "localization" / f"{task_id}.probe.json" + + +def _search_all( + modality: str, + query: str, + media_id: str, + expected_start: float, + expected_end: float, + *, + config: IndexConfig, + runtime: ModelRuntimePort, + storage: IndexStore, + filters: Mapping[str, Any] | None = None, +) -> tuple[SearchResult, dict[str, Any]]: + record_count = storage.count_records( + modality, + video_id=media_id, + filters=filters, + ) + if record_count == 0: + raise RuntimeError(f"no {modality} records are indexed for this task") + started = time.perf_counter() + result = SEARCHERS[modality]( + query, + config=config, + runtime=runtime, + top_k=record_count, + video_id=media_id, + filters=filters, + storage=storage, + ) + elapsed_seconds = time.perf_counter() - started + records = [ + { + "start_seconds": hit.start, + "end_seconds": hit.end, + "retrieval_rank": hit.rank, + "ordering_score": hit.score, + "raw_distance": hit.raw_distance, + "source_id": hit.source_id, + "metadata": hit.metadata, + } + for hit in sorted(result.hits, key=lambda item: (item.start, item.end)) + ] + top_hit = result.hits[0] + best_hit = max( + result.hits, + key=lambda hit: interval_iou( + hit.start, + hit.end, + expected_start, + expected_end, + ), + ) + return result, { + "model": MODELS[modality].identity(), + "record_count": len(records), + "elapsed_seconds": elapsed_seconds, + "model_calls": {"text_embedding": 1}, + "top_retrieved": { + "start_seconds": top_hit.start, + "end_seconds": top_hit.end, + "temporal_iou": interval_iou( + top_hit.start, + top_hit.end, + expected_start, + expected_end, + ), + }, + "best_individual_interval_oracle": { + "start_seconds": best_hit.start, + "end_seconds": best_hit.end, + "retrieval_rank": best_hit.rank, + "temporal_iou": interval_iou( + best_hit.start, + best_hit.end, + expected_start, + expected_end, + ), + }, + "records": records, + } + + +def export_probe( + task_id: str, + output: Path | None = None, + *, + current_top_k: int = 3, +) -> dict[str, Any]: + if current_top_k <= 0: + raise ValueError("current_top_k must be positive") + _load_environment() + task = _task(task_id) + context = create_local_application( + repository_name=os.environ.get("VIDXP_EVAL_REPOSITORY", "default"), + index_directory=_required_environment("VIDXP_EVAL_INDEX_DIR"), + data_directory=_required_environment("VIDXP_EVAL_DATA_DIR"), + device=os.environ.get("VIDXP_EVAL_DEVICE", "cpu"), + ) + application = context.application + filename = Path(task["media_relpath"]).name + page = application.media.list( + ListMediaCommand( + page_size=2, + filename=filename, + state=MediaState.ready, + ) + ) + if len(page.items) != 1: + raise RuntimeError(f"expected one ready media record for {filename}") + media_id = page.items[0].media_id + config = application.index_backend.active_config( + application.index_directory, + device=application.device, + ) + modalities = tuple( + modality + for modality in task["modalities"] + if modality in SEARCHERS and modality in config.enabled_modalities + ) + if not modalities: + raise RuntimeError("the task has no indexed searchable modalities") + + started = time.perf_counter() + with application.index_backend.open_store(config) as storage: + with application.runtime.scheduler.inference(): + searched = { + modality: _search_all( + modality, + task["query"], + media_id, + float(task["expected_start"]), + float(task["expected_end"]), + config=config, + runtime=application.runtime, + storage=storage, + ) + for modality in modalities + } + elapsed_seconds = time.perf_counter() - started + full_results = tuple(searched[name][0] for name in modalities) + probe_results = {name: searched[name][1] for name in modalities} + current_inputs = tuple( + result.model_copy(update={"hits": result.hits[:current_top_k]}) + for result in full_results + ) + current_fusion = fuse_search_results( + query=task["query"], + requested_modalities=modalities, + results=current_inputs, + media_id=media_id, + top_k=current_top_k, + snapshot_id=config.snapshot_id, + ) + top_moment = current_fusion.moments[0] if current_fusion.moments else None + current_metrics = ( + { + "temporal_iou": interval_iou( + top_moment.start, + top_moment.end, + float(task["expected_start"]), + float(task["expected_end"]), + ), + "start_error_seconds": top_moment.start - float(task["expected_start"]), + "end_error_seconds": top_moment.end - float(task["expected_end"]), + "duration_error_seconds": ( + top_moment.end + - top_moment.start + - float(task["expected_end"]) + + float(task["expected_start"]) + ), + } + if top_moment is not None + else None + ) + payload = { + "schema_version": 1, + "task_id": task_id, + "video_id": task["video_id"], + "media_id": media_id, + "query": task["query"], + "expected_start": task["expected_start"], + "expected_end": task["expected_end"], + "snapshot_id": config.snapshot_id, + "vector_distance": config.vector_distance, + "score_definition": "ordering_score is negative raw_distance", + "modalities": probe_results, + "current_control": { + "candidate_top_k_per_modality": current_top_k, + "output_top_k": current_top_k, + "top_moment_metrics": current_metrics, + "result": current_fusion.model_dump(mode="json"), + }, + "elapsed_seconds": elapsed_seconds, + } + destination = _output_path(task_id, output) + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text( + json.dumps(payload, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + return { + "output": str(destination), + "expected_interval": { + "start_seconds": task["expected_start"], + "end_seconds": task["expected_end"], + }, + "current_control": { + "top_interval": ( + { + "start_seconds": top_moment.start, + "end_seconds": top_moment.end, + } + if top_moment is not None + else None + ), + "metrics": current_metrics, + }, + "modalities": { + name: { + "records": result["record_count"], + "elapsed_seconds": result["elapsed_seconds"], + "model_calls": result["model_calls"], + "top_retrieved": result["top_retrieved"], + "best_individual_interval_oracle": result[ + "best_individual_interval_oracle" + ], + } + for name, result in probe_results.items() + }, + "model_calls": { + "text_embedding": sum( + result["model_calls"]["text_embedding"] + for result in probe_results.values() + ) + }, + "elapsed_seconds": elapsed_seconds, + } + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Export all indexed evidence scores for one benchmark task." + ) + parser.add_argument("task_id") + parser.add_argument("--output", type=Path) + parser.add_argument("--top-k", type=int, default=3) + arguments = parser.parse_args() + print( + json.dumps( + export_probe( + arguments.task_id, + arguments.output, + current_top_k=arguments.top_k, + ), + indent=2, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/preflight.mjs b/benchmarks/codex-mcp/scripts/preflight.mjs index 7d464939..2638f27e 100644 --- a/benchmarks/codex-mcp/scripts/preflight.mjs +++ b/benchmarks/codex-mcp/scripts/preflight.mjs @@ -1,10 +1,23 @@ -import { existsSync, readFileSync } from 'node:fs'; +import { existsSync, readFileSync, rmSync, statSync } from 'node:fs'; import { isAbsolute, join, resolve } from 'node:path'; import { spawnSync } from 'node:child_process'; import { fileURLToPath } from 'node:url'; +import { + EVALUATION_PERMISSION_PROFILE, + evaluationPermissionConfigs, +} from './condition-state.mjs'; const benchmarkRoot = resolve(fileURLToPath(new URL('..', import.meta.url))); +const repositoryRoot = resolve(benchmarkRoot, '..', '..'); const manifestPath = join(benchmarkRoot, 'tasks', 'longvale-part9-pilot.json'); +const codexLauncher = join( + benchmarkRoot, + 'node_modules', + '@openai', + 'codex', + 'bin', + 'codex.js', +); const requiredNode = [22, 22, 0]; const currentNode = process.versions.node.split('.').map(Number); @@ -36,45 +49,286 @@ function requireFile(name) { } const codexHome = requireDirectory('VIDXP_EVAL_CODEX_HOME'); +const vidxpOnCodexHome = requireDirectory('VIDXP_EVAL_VIDXP_ON_CODEX_HOME'); +const vidxpOffCodexHome = requireDirectory('VIDXP_EVAL_VIDXP_OFF_CODEX_HOME'); +const cleanUserCodexHome = requireDirectory('VIDXP_EVAL_CLEAN_USER_CODEX_HOME'); const workspace = requireDirectory('VIDXP_EVAL_WORKSPACE'); -const dataDirectory = requireDirectory('VIDXP_EVAL_DATA_DIR'); -const indexDirectory = requireDirectory('VIDXP_EVAL_INDEX_DIR'); -const mcpCommand = requireFile('VIDXP_MCP_COMMAND'); +const vidxpOnWorkspace = requireDirectory('VIDXP_EVAL_VIDXP_ON_WORKSPACE'); +const vidxpOffWorkspace = requireDirectory('VIDXP_EVAL_VIDXP_OFF_WORKSPACE'); +const cleanUserWorkspace = requireDirectory('VIDXP_EVAL_CLEAN_USER_WORKSPACE'); +requireDirectory('VIDXP_EVAL_DATA_DIR'); +requireDirectory('VIDXP_EVAL_INDEX_DIR'); +requireDirectory('VIDXP_MODEL_CACHE'); +const uvCacheDirectory = requireDirectory('VIDXP_EVAL_UV_CACHE_DIR'); +requireFile('VIDXP_MCP_COMMAND'); +const promptfooPython = requireFile('PROMPTFOO_PYTHON'); +if (!existsSync(codexLauncher)) { + throw new Error(`The pinned Codex launcher is missing: ${codexLauncher}`); +} +const machineId = process.env.VIDXP_EVAL_MACHINE_ID; +if (!machineId || !/^[a-z0-9]+(?:-[a-z0-9]+)*$/.test(machineId)) { + throw new Error( + 'VIDXP_EVAL_MACHINE_ID must be a stable repository machine ID; rerun setup with --machine-id.', + ); +} if (!existsSync(join(codexHome, 'auth.json'))) { - throw new Error('The isolated Codex home has no auth.json; sign in there before evaluating.'); + throw new Error('The isolated authentication home has no auth.json; run setup first.'); +} + +const scorerRuntime = spawnSync( + promptfooPython, + [ + '-c', + [ + 'import vidxp.composition', + 'import vidxp.infrastructure.dbos_jobs', + 'import vidxp.workflow_runtime', + ].join('; '), + ], + { cwd: repositoryRoot, encoding: 'utf8', stdio: 'pipe' }, +); +if (scorerRuntime.status !== 0) { + throw new Error( + `Promptfoo scorer runtime cannot import VidXP:\n${scorerRuntime.stderr + || scorerRuntime.stdout + || scorerRuntime.error?.message}`, + ); } -const codexConfig = join(codexHome, 'config.toml'); -if (existsSync(codexConfig)) { - const content = readFileSync(codexConfig, 'utf8'); - if (/^\s*\[mcp_servers(?:\.|\])/m.test(content)) { - throw new Error('The isolated Codex home config contains ambient MCP servers.'); +const permissionConfigs = evaluationPermissionConfigs(process.env); +for (const [condition, conditionHome] of Object.entries({ + vidxpOn: vidxpOnCodexHome, + vidxpOff: vidxpOffCodexHome, + cleanUser: cleanUserCodexHome, +})) { + if (!existsSync(join(conditionHome, 'auth.json'))) { + throw new Error(`Condition Codex home has no auth.json: ${conditionHome}`); + } + const codexConfig = join(conditionHome, 'config.toml'); + if ( + !existsSync(codexConfig) + || readFileSync(codexConfig, 'utf8') !== permissionConfigs.configs[condition] + ) { + throw new Error(`Condition Codex isolation is missing or stale: ${conditionHome}`); } } const tasks = JSON.parse(readFileSync(manifestPath, 'utf8')); -const missingMedia = [...new Set(tasks - .map((task) => join(workspace, task.media_relpath)) +const conditionWorkspaces = [vidxpOffWorkspace, cleanUserWorkspace]; +const missingMedia = [...new Set([workspace, ...conditionWorkspaces] + .flatMap((conditionWorkspace) => tasks + .map((task) => join(conditionWorkspace, task.media_relpath))) .filter((path) => !existsSync(path)))]; if (missingMedia.length > 0) { throw new Error(`Pilot media is missing:\n${missingMedia.join('\n')}`); } +for (const task of tasks) { + const shared = statSync(join(workspace, task.media_relpath)); + const off = statSync(join(vidxpOffWorkspace, task.media_relpath)); + const cleanUser = statSync(join(cleanUserWorkspace, task.media_relpath)); + if ( + off.dev !== shared.dev + || off.ino !== shared.ino + || cleanUser.dev !== shared.dev + || cleanUser.ino !== shared.ino + ) { + throw new Error( + `Condition media is not hard-linked to the shared bytes: ${task.media_relpath}`, + ); + } + if (existsSync(join(vidxpOnWorkspace, task.media_relpath))) { + throw new Error( + `VidXP-on exposes source media that would permit a shell bypass: ${task.media_relpath}`, + ); + } +} + +const sourceSkillDirectory = join( + repositoryRoot, + 'plugins', + 'vidxp', + 'skills', + 'vidxp-find-video-evidence', +); +const onSkillDirectory = join( + vidxpOnWorkspace, + '.agents', + 'skills', + 'vidxp-find-video-evidence', +); +const offSkillDirectory = join( + vidxpOffWorkspace, + '.agents', + 'skills', + 'vidxp-find-video-evidence', +); +const cleanUserSkillDirectory = join( + cleanUserWorkspace, + '.agents', + 'skills', + 'vidxp-find-video-evidence', +); +const sharedSkillDirectory = join( + workspace, + '.agents', + 'skills', + 'vidxp-find-video-evidence', +); +for (const relativePath of ['SKILL.md', join('agents', 'openai.yaml')]) { + const source = join(sourceSkillDirectory, relativePath); + const installed = join(onSkillDirectory, relativePath); + const matchesCommittedSkill = existsSync(installed) + && readFileSync(installed, 'utf8') === readFileSync(source, 'utf8'); + if (!matchesCommittedSkill) { + throw new Error( + `The VidXP-on workspace does not contain the committed ${relativePath}.`, + ); + } +} +if (existsSync(offSkillDirectory)) { + throw new Error('The VidXP-off workspace must not contain the VidXP evidence skill.'); +} +if (existsSync(cleanUserSkillDirectory)) { + throw new Error('The clean-user workspace must not contain the VidXP evidence skill.'); +} +if (existsSync(sharedSkillDirectory)) { + throw new Error('The shared parent workspace must not contain the VidXP evidence skill.'); +} + +const cleanPath = process.env.VIDXP_EVAL_CLEAN_USER_PATH; +if (!cleanPath) { + throw new Error('VIDXP_EVAL_CLEAN_USER_PATH is required.'); +} + +if (process.platform === 'darwin') { + function verifySandbox({ home, conditionWorkspace, allowedPath, ffmpegAllowed, path }) { + const writeProbe = join(conditionWorkspace, 'tmp', '.vidxp-isolation-probe'); + rmSync(writeProbe, { force: true }); + const script = [ + 'set -eu', + 'if /bin/cat "$VIDXP_PROBE_DENIED" >/dev/null 2>&1; then exit 41; fi', + '/bin/cat "$VIDXP_PROBE_ALLOWED" >/dev/null', + '/usr/bin/touch "$VIDXP_PROBE_WRITE"', + ffmpegAllowed + ? '"$VIDXP_PROBE_FFMPEG" -version >/dev/null 2>&1' + : 'if "$VIDXP_PROBE_FFMPEG" -version >/dev/null 2>&1; then exit 42; fi', + ].join('\n'); + const result = spawnSync( + process.execPath, + [ + codexLauncher, + 'sandbox', + '--permission-profile', + EVALUATION_PERMISSION_PROFILE, + '--cd', + conditionWorkspace, + '/bin/zsh', + '-c', + script, + ], + { + cwd: conditionWorkspace, + env: { + CODEX_HOME: home, + HOME: conditionWorkspace, + PATH: path, + TMPDIR: join(conditionWorkspace, 'tmp'), + VIDXP_PROBE_ALLOWED: allowedPath, + VIDXP_PROBE_DENIED: join(repositoryRoot, 'README.md'), + VIDXP_PROBE_FFMPEG: permissionConfigs.ffmpeg, + VIDXP_PROBE_WRITE: writeProbe, + }, + encoding: 'utf8', + stdio: 'pipe', + }, + ); + rmSync(writeProbe, { force: true }); + if (result.status !== 0) { + throw new Error( + `Codex did not enforce the ${EVALUATION_PERMISSION_PROFILE} profile in ${conditionWorkspace}:\n` + + (result.stderr || result.stdout || result.error?.message), + ); + } + } + + const firstMediaPath = join(vidxpOffWorkspace, tasks[0].media_relpath); + verifySandbox({ + home: vidxpOnCodexHome, + conditionWorkspace: vidxpOnWorkspace, + allowedPath: join(onSkillDirectory, 'SKILL.md'), + ffmpegAllowed: false, + path: process.env.PATH, + }); + verifySandbox({ + home: vidxpOffCodexHome, + conditionWorkspace: vidxpOffWorkspace, + allowedPath: firstMediaPath, + ffmpegAllowed: true, + path: process.env.PATH, + }); + verifySandbox({ + home: cleanUserCodexHome, + conditionWorkspace: cleanUserWorkspace, + allowedPath: join(cleanUserWorkspace, tasks[0].media_relpath), + ffmpegAllowed: false, + path: cleanPath, + }); +} + +if (process.platform !== 'win32') { + const cleanShell = spawnSync( + '/bin/zsh', + [ + '-lc', + 'for name in ffmpeg ffprobe vidxp vidxp-mcp; do ' + + 'if command -v "$name" >/dev/null 2>&1; then exit 42; fi; done; ' + + 'command -v curl >/dev/null', + ], + { + cwd: cleanUserWorkspace, + env: { + HOME: cleanUserWorkspace, + PATH: cleanPath, + TMPDIR: join(cleanUserWorkspace, 'tmp'), + }, + encoding: 'utf8', + stdio: 'pipe', + }, + ); + if (cleanShell.status !== 0) { + throw new Error( + cleanShell.status === 42 + ? 'The clean-user shell exposes a preinstalled media or VidXP executable.' + : `The clean-user shell probe failed: ${cleanShell.stderr || cleanShell.error?.message}`, + ); + } +} const check = spawnSync( - mcpCommand, + process.platform === 'win32' ? 'uv.exe' : 'uv', [ - '--check', - '--repository', process.env.VIDXP_EVAL_REPOSITORY || 'default', - '--index-directory', indexDirectory, - '--data-dir', dataDirectory, - '--device', process.env.VIDXP_EVAL_DEVICE || 'cpu', + 'run', '--no-sync', 'python', + join(benchmarkRoot, 'scripts', 'mcp_preflight.py'), ], - { encoding: 'utf8', stdio: 'pipe' }, + { + cwd: repositoryRoot, + env: { + ...process.env, + UV_CACHE_DIR: uvCacheDirectory, + VIDXP_ALLOW_MODEL_DOWNLOADS: 'false', + }, + encoding: 'utf8', + stdio: 'pipe', + }, ); if (check.status !== 0) { - throw new Error(`VidXP MCP preflight failed:\n${check.stderr || check.stdout}`); + throw new Error( + `VidXP MCP preflight failed:\n${check.stderr || check.stdout || check.error?.message}`, + ); } process.stdout.write(check.stdout); -process.stdout.write(`Ready: ${tasks.length} tasks, 2 conditions, no model calls made.\n`); +process.stdout.write( + `Ready on ${machineId}: ${tasks.length} tasks across VidXP, direct-local, and clean-user conditions; no Codex or model inference calls made.\n`, +); diff --git a/benchmarks/codex-mcp/scripts/query_routing_control.py b/benchmarks/codex-mcp/scripts/query_routing_control.py new file mode 100644 index 00000000..62c237de --- /dev/null +++ b/benchmarks/codex-mcp/scripts/query_routing_control.py @@ -0,0 +1,493 @@ +from __future__ import annotations + +import json +import os +import time +from pathlib import Path +from typing import Any + +from vidxp.application_models import ListMediaCommand, MediaState +from vidxp.benchmarks.agent_ablation_score import interval_iou +from vidxp.composition import create_local_application +from vidxp.core.contracts import IndexConfig +from vidxp.ports import IndexStore, ModelRuntimePort + +from modality_probe import ( + BENCHMARK_ROOT, + _load_environment, + _output_path, + _required_environment, + _search_all, + _task, +) + + +QUERY_PLAN_PATH = ( + BENCHMARK_ROOT / "tasks" / "longvale-part9-modality-queries.json" +) + + +def _rank_metrics( + records: list[dict[str, Any]], + *, + expected_start: float, + expected_end: float, + top_k: int = 3, +) -> dict[str, Any]: + ranked = sorted(records, key=lambda item: int(item["retrieval_rank"])) + scored = [ + ( + item, + interval_iou( + float(item["start_seconds"]), + float(item["end_seconds"]), + expected_start, + expected_end, + ), + ) + for item in ranked + ] + overlapping = [ + (item, iou) + for item, iou in scored + if iou > 0 + ] + first_overlapping = min( + overlapping, + key=lambda pair: int(pair[0]["retrieval_rank"]), + default=None, + ) + best_iou = max((iou for _, iou in scored), default=0.0) + best_iou_rank = min( + ( + int(item["retrieval_rank"]) + for item, iou in scored + if abs(iou - best_iou) <= 1e-12 + ), + default=None, + ) + top_k_best_iou = max( + (iou for item, iou in scored if int(item["retrieval_rank"]) <= top_k), + default=0.0, + ) + return { + "first_overlapping_rank": ( + int(first_overlapping[0]["retrieval_rank"]) + if first_overlapping is not None + else None + ), + "first_overlapping_interval": ( + { + "start_seconds": float(first_overlapping[0]["start_seconds"]), + "end_seconds": float(first_overlapping[0]["end_seconds"]), + "temporal_iou": first_overlapping[1], + "representation": first_overlapping[0]["metadata"].get( + "representation" + ), + } + if first_overlapping is not None + else None + ), + "best_interval_iou": best_iou, + "best_interval_rank": best_iou_rank, + "top_k": top_k, + "top_k_best_interval_iou": top_k_best_iou, + "top_k_contains_best_interval": ( + best_iou_rank is not None and best_iou_rank <= top_k + ), + "top_k_contains_overlapping_interval": ( + first_overlapping is not None + and int(first_overlapping[0]["retrieval_rank"]) <= top_k + ), + } + + +def _change(before: int | None, after: int | None) -> str: + if before is None or after is None: + return "unavailable" + if after < before: + return "improved" + if after > before: + return "worse" + return "unchanged" + + +def _report_path() -> Path: + data_directory = Path(_required_environment("VIDXP_EVAL_DATA_DIR")) + return data_directory.parent / "localization" / "query-routing-held-out.json" + + +def _search_metrics( + modality: str, + query: str, + media_id: str, + expected_start: float, + expected_end: float, + *, + config: IndexConfig, + runtime: ModelRuntimePort, + storage: IndexStore, + representation: str | None = None, +) -> dict[str, Any]: + filters = ( + {"representation": representation} + if representation is not None + else None + ) + _, result = _search_all( + modality, + query, + media_id, + expected_start, + expected_end, + config=config, + runtime=runtime, + storage=storage, + filters=filters, + ) + return { + "query": query, + "record_count": result["record_count"], + "metrics": _rank_metrics( + result["records"], + expected_start=expected_start, + expected_end=expected_end, + ), + "elapsed_seconds": result["elapsed_seconds"], + } + + +def compare_query_routing() -> dict[str, Any]: + _load_environment() + query_plan = json.loads(QUERY_PLAN_PATH.read_text(encoding="utf-8")) + planned_tasks = query_plan["tasks"] + context = create_local_application( + repository_name=os.environ.get("VIDXP_EVAL_REPOSITORY", "default"), + index_directory=_required_environment("VIDXP_EVAL_INDEX_DIR"), + data_directory=_required_environment("VIDXP_EVAL_DATA_DIR"), + device=os.environ.get("VIDXP_EVAL_DEVICE", "cpu"), + ) + application = context.application + config = application.index_backend.active_config( + application.index_directory, + device=application.device, + ) + comparisons: list[dict[str, Any]] = [] + sound_stream_comparisons: list[dict[str, Any]] = [] + started = time.perf_counter() + model_calls = 0 + + with application.index_backend.open_store(config) as storage: + with application.runtime.scheduler.inference(): + for task_id, routed_queries in planned_tasks.items(): + task = _task(task_id) + baseline_path = _output_path(task_id, None) + if not baseline_path.is_file(): + raise RuntimeError( + f"saved full-query probe is missing for {task_id}" + ) + baseline = json.loads(baseline_path.read_text(encoding="utf-8")) + if baseline["query"] != task["query"]: + raise RuntimeError(f"saved query does not match {task_id}") + if baseline["snapshot_id"] != config.snapshot_id: + raise RuntimeError(f"saved snapshot is stale for {task_id}") + filename = Path(task["media_relpath"]).name + page = application.media.list( + ListMediaCommand( + page_size=2, + filename=filename, + state=MediaState.ready, + ) + ) + if len(page.items) != 1: + raise RuntimeError( + f"expected one ready media record for {filename}" + ) + media_id = page.items[0].media_id + expected_modalities = set(task["modalities"]) + if set(routed_queries) != expected_modalities: + raise RuntimeError( + f"query plan does not cover task modalities for {task_id}" + ) + + for modality in task["modalities"]: + routed_query = str(routed_queries[modality]) + routed = _search_metrics( + modality, + routed_query, + media_id, + float(task["expected_start"]), + float(task["expected_end"]), + config=config, + runtime=application.runtime, + storage=storage, + ) + model_calls += 1 + baseline_metrics = _rank_metrics( + baseline["modalities"][modality]["records"], + expected_start=float(task["expected_start"]), + expected_end=float(task["expected_end"]), + ) + routed_metrics = routed["metrics"] + comparisons.append( + { + "task_id": task_id, + "modality": modality, + "full_query": task["query"], + "routed_query": routed_query, + "full_query_metrics": baseline_metrics, + "routed_query_metrics": routed_metrics, + "best_interval_rank_change": _change( + baseline_metrics["best_interval_rank"], + routed_metrics["best_interval_rank"], + ), + "first_overlap_rank_change": _change( + baseline_metrics["first_overlapping_rank"], + routed_metrics["first_overlapping_rank"], + ), + "elapsed_seconds": routed["elapsed_seconds"], + } + ) + if modality == "sound": + stream_specs = { + "window_caption_query": ( + "window", + str(task["query"]), + ), + "window_phrase_query": ("window", routed_query), + "activation_full_query": ( + "activation", + str(task["query"]), + ), + "activation_phrase_query": ( + "activation", + routed_query, + ), + } + streams = { + name: _search_metrics( + modality, + query, + media_id, + float(task["expected_start"]), + float(task["expected_end"]), + config=config, + runtime=application.runtime, + storage=storage, + representation=representation, + ) + for name, (representation, query) in stream_specs.items() + } + model_calls += len(stream_specs) + sound_stream_comparisons.append( + { + "task_id": task_id, + "current_mixed_full_query": baseline_metrics, + **streams, + } + ) + + elapsed_seconds = time.perf_counter() - started + change_counts = { + name: sum( + item["best_interval_rank_change"] == name for item in comparisons + ) + for name in ("improved", "unchanged", "worse", "unavailable") + } + baseline_top3 = sum( + item["full_query_metrics"]["top_k_contains_best_interval"] + for item in comparisons + ) + routed_top3 = sum( + item["routed_query_metrics"]["top_k_contains_best_interval"] + for item in comparisons + ) + baseline_overlap_top3 = sum( + item["full_query_metrics"]["top_k_contains_overlapping_interval"] + for item in comparisons + ) + routed_overlap_top3 = sum( + item["routed_query_metrics"]["top_k_contains_overlapping_interval"] + for item in comparisons + ) + overlap_change_counts = { + name: sum(item["first_overlap_rank_change"] == name for item in comparisons) + for name in ("improved", "unchanged", "worse", "unavailable") + } + current_sound_overlap_top3 = sum( + item["current_mixed_full_query"]["top_k_contains_overlapping_interval"] + for item in sound_stream_comparisons + ) + separated_sound_overlap_top3 = sum( + item[stream]["metrics"]["top_k_contains_overlapping_interval"] + for item in sound_stream_comparisons + for stream in ("window_caption_query", "activation_phrase_query") + ) + separated_sound_tasks_with_overlap_top3 = sum( + any( + item[stream]["metrics"]["top_k_contains_overlapping_interval"] + for stream in ("window_caption_query", "activation_phrase_query") + ) + for item in sound_stream_comparisons + ) + one_phrase_sound_tasks_with_overlap_top3 = sum( + any( + item[stream]["metrics"]["top_k_contains_overlapping_interval"] + for stream in ("window_phrase_query", "activation_phrase_query") + ) + for item in sound_stream_comparisons + ) + report = { + "schema_version": 1, + "control": { + "id": query_plan["method"], + "type": "benchmark-only manual wording ceiling", + "constraints": query_plan["constraints"], + "research_relationship": ( + "Luo et al. (WACV 2024) and TFVTG (ECCV 2024) motivate " + "compound-query decomposition; per-modality phrases are a " + "VidXP diagnostic and are not either paper's method." + ), + }, + "task_count": len(planned_tasks), + "task_modality_pairs": len(comparisons), + "summary": { + "best_interval_rank_changes": change_counts, + "first_overlap_rank_changes": overlap_change_counts, + "full_query_top3_contains_best_interval": baseline_top3, + "routed_query_top3_contains_best_interval": routed_top3, + "full_query_top3_contains_target_overlap": baseline_overlap_top3, + "routed_query_top3_contains_target_overlap": routed_overlap_top3, + "current_sound_tasks_with_target_overlap_top3": ( + current_sound_overlap_top3 + ), + "separated_sound_stream_hits_in_top3": separated_sound_overlap_top3, + "separated_sound_tasks_with_target_overlap_top3": ( + separated_sound_tasks_with_overlap_top3 + ), + "one_phrase_separated_sound_tasks_with_target_overlap_top3": ( + one_phrase_sound_tasks_with_overlap_top3 + ), + }, + "resource_use": { + "local_text_embedding_calls": model_calls, + "elapsed_seconds": elapsed_seconds, + "codex_or_api_calls": 0, + "index_bytes_written": 0, + }, + "comparisons": comparisons, + "sound_stream_comparisons": sound_stream_comparisons, + } + destination = _report_path() + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text( + json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + report["output"] = str(destination) + return report + + +def _print_report(report: dict[str, Any]) -> None: + summary = report["summary"] + changes = summary["best_interval_rank_changes"] + overlap_changes = summary["first_overlap_rank_changes"] + print("Modality-query ranking control") + print( + "Target-overlapping evidence in top 3: " + f"full query {summary['full_query_top3_contains_target_overlap']}/" + f"{report['task_modality_pairs']}; routed query " + f"{summary['routed_query_top3_contains_target_overlap']}/" + f"{report['task_modality_pairs']}" + ) + print( + "First target-overlap rank: " + f"{overlap_changes['improved']} improved, " + f"{overlap_changes['unchanged']} unchanged, " + f"{overlap_changes['worse']} worse" + ) + print( + "Best-boundary record in top 3: " + f"full query {summary['full_query_top3_contains_best_interval']}/" + f"{report['task_modality_pairs']}; routed query " + f"{summary['routed_query_top3_contains_best_interval']}/" + f"{report['task_modality_pairs']}" + ) + print( + "Best-boundary rank: " + f"{changes['improved']} improved, {changes['unchanged']} unchanged, " + f"{changes['worse']} worse" + ) + print() + print( + f"{'Task / modality':39} {'target rank':>20} " + f"{'boundary rank':>20} query" + ) + for item in report["comparisons"]: + task_name = item["task_id"].removeprefix("longvale-part9-") + label = f"{task_name} / {item['modality']}" + full_target = item["full_query_metrics"]["first_overlapping_rank"] + routed_target = item["routed_query_metrics"]["first_overlapping_rank"] + full_boundary = item["full_query_metrics"]["best_interval_rank"] + routed_boundary = item["routed_query_metrics"]["best_interval_rank"] + print( + f"{label:39} {f'{full_target} -> {routed_target}':>20} " + f"{f'{full_boundary} -> {routed_boundary}':>20} " + f"{item['routed_query']}" + ) + print() + print("FineLAP paths kept separate") + print( + f"{'Task':27} {'mixed':>7} {'window':>17} {'activation':>21}" + ) + print(f"{'':27} {'':>7} {'full / phrase':>17} {'full / phrase':>21}") + for item in report["sound_stream_comparisons"]: + label = item["task_id"].removeprefix("longvale-part9-") + mixed_rank = item["current_mixed_full_query"]["first_overlapping_rank"] + window_rank = item["window_caption_query"]["metrics"][ + "first_overlapping_rank" + ] + window_phrase_rank = item["window_phrase_query"]["metrics"][ + "first_overlapping_rank" + ] + activation_full_rank = item["activation_full_query"]["metrics"][ + "first_overlapping_rank" + ] + activation_rank = item["activation_phrase_query"]["metrics"][ + "first_overlapping_rank" + ] + print( + f"{label:27} {str(mixed_rank):>7} " + f"{f'{window_rank} / {window_phrase_rank}':>17} " + f"{f'{activation_full_rank} / {activation_rank}':>21}" + ) + print( + "Sound tasks with target evidence in a top 3: " + f"mixed {summary['current_sound_tasks_with_target_overlap_top3']}/" + f"{len(report['sound_stream_comparisons'])}; separated " + f"{summary['separated_sound_tasks_with_target_overlap_top3']}/" + f"{len(report['sound_stream_comparisons'])}" + ) + print( + "Using the short phrase for both separated streams: " + f"{summary['one_phrase_separated_sound_tasks_with_target_overlap_top3']}/" + f"{len(report['sound_stream_comparisons'])} sound tasks" + ) + resources = report["resource_use"] + print() + print( + f"Local embeddings: {resources['local_text_embedding_calls']}; " + f"time: {resources['elapsed_seconds']:.3f}s; " + "Codex/API calls: 0" + ) + print(f"Full evidence: {report['output']}") + + +def main() -> int: + report = compare_query_routing() + _print_report(report) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/report.mjs b/benchmarks/codex-mcp/scripts/report.mjs new file mode 100644 index 00000000..46654326 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/report.mjs @@ -0,0 +1,1280 @@ +import { homedir } from 'node:os'; +import { join, resolve } from 'node:path'; +import { existsSync, readFileSync, readdirSync } from 'node:fs'; +import { fileURLToPath } from 'node:url'; +import { DatabaseSync } from 'node:sqlite'; +import { spawnSync } from 'node:child_process'; + +const CONDITION_ORDER = [ + 'vidxp-on', + 'vidxp-off', + 'clean-user', + 'local-slm', + 'local-slm-planner', +]; + +function parseJson(value, fallback = {}) { + if (typeof value !== 'string') { + return fallback; + } + try { + return JSON.parse(value); + } catch { + return fallback; + } +} + +function mean(values) { + const numbers = values.filter((value) => Number.isFinite(value)); + return numbers.length === 0 + ? null + : numbers.reduce((total, value) => total + value, 0) / numbers.length; +} + +function sum(values) { + return values + .filter((value) => Number.isFinite(value)) + .reduce((total, value) => total + value, 0); +} + +function sumOrNull(values) { + const numbers = values.filter((value) => Number.isFinite(value)); + return numbers.length === 0 + ? null + : numbers.reduce((total, value) => total + value, 0); +} + +function fixed(value, digits = 3) { + return Number.isFinite(value) ? value.toFixed(digits) : 'n/a'; +} + +function seconds(milliseconds) { + return Number.isFinite(milliseconds) ? `${(milliseconds / 1000).toFixed(3)}s` : 'n/a'; +} + +function secondsValue(value) { + return Number.isFinite(value) ? `${value.toFixed(3)}s` : 'n/a'; +} + +function integer(value) { + return Number.isFinite(value) ? Math.round(value).toLocaleString('en-US') : 'n/a'; +} + +function money(value, digits = 6) { + return Number.isFinite(value) ? `$${value.toFixed(digits)}` : 'n/a'; +} + +function tokenDifference(total, cached) { + return Number.isFinite(total) && Number.isFinite(cached) + ? Math.max(0, total - cached) + : null; +} + +function conditionCodexHome(condition) { + const byCondition = { + 'vidxp-on': process.env.VIDXP_EVAL_VIDXP_ON_CODEX_HOME, + 'vidxp-off': process.env.VIDXP_EVAL_VIDXP_OFF_CODEX_HOME, + 'clean-user': process.env.VIDXP_EVAL_CLEAN_USER_CODEX_HOME, + }; + return byCondition[condition] || process.env.VIDXP_EVAL_CODEX_HOME; +} + +function findRollout(codexHome, sessionId) { + const sessions = codexHome && join(codexHome, 'sessions'); + if (!sessions || !existsSync(sessions) || !sessionId) { + return null; + } + const pending = [sessions]; + while (pending.length > 0) { + const directory = pending.pop(); + for (const entry of readdirSync(directory, { withFileTypes: true })) { + const path = join(directory, entry.name); + if (entry.isDirectory()) { + pending.push(path); + } else if (entry.isFile() && entry.name.endsWith(`${sessionId}.jsonl`)) { + return path; + } + } + } + return null; +} + +function rolloutModelTurns(condition, sessionId) { + const conditionHome = conditionCodexHome(condition); + const path = findRollout(conditionHome, sessionId) + || (conditionHome === process.env.VIDXP_EVAL_CODEX_HOME + ? null + : findRollout(process.env.VIDXP_EVAL_CODEX_HOME, sessionId)); + if (!path) { + return null; + } + let modelTurns = 0; + let lastTotal = -1; + for (const line of readFileSync(path, 'utf8').split('\n')) { + if (!line) continue; + const event = parseJson(line, null); + const payload = event?.payload; + if (event?.type === 'event_msg' && payload?.type === 'token_count') { + const info = payload.info; + const cumulative = Number(info?.total_token_usage?.total_tokens); + if (info?.last_token_usage && cumulative > lastTotal) { + modelTurns += 1; + lastTotal = cumulative; + } + } + } + return modelTurns; +} + +export function summarizeRecordedItems(raw) { + const turn = typeof raw === 'string' ? parseJson(raw, null) : raw; + const items = Array.isArray(turn?.items) ? turn.items : null; + if (!items) { + return null; + } + let toolCalls = 0; + let mcpCalls = 0; + let shellCalls = 0; + for (const item of items) { + if (item?.type === 'command_execution') { + shellCalls += 1; + toolCalls += 1; + } else if (typeof item?.type === 'string' && item.type.endsWith('_tool_call')) { + toolCalls += 1; + } + if (item?.type === 'mcp_tool_call' && item.server === 'vidxp') { + mcpCalls += 1; + } + } + return { + agentItems: items.length, + toolCalls, + mcpCalls, + shellCalls, + }; +} + +function boundaryError(predicted, expected) { + return Number.isFinite(predicted) && Number.isFinite(expected) + ? predicted - expected + : null; +} + +function absolute(value) { + return Number.isFinite(value) ? Math.abs(value) : null; +} + +function durationError(result) { + if ( + !Number.isFinite(result.predictedStart) + || !Number.isFinite(result.predictedEnd) + || !Number.isFinite(result.expectedStart) + || !Number.isFinite(result.expectedEnd) + ) { + return null; + } + return (result.predictedEnd - result.predictedStart) + - (result.expectedEnd - result.expectedStart); +} + +function interval(start, end) { + return Number.isFinite(start) && Number.isFinite(end) + ? `${start.toFixed(3)}ā€“${end.toFixed(3)}s` + : 'n/a'; +} + +function intervalIou(start, end, expectedStart, expectedEnd) { + if (![start, end, expectedStart, expectedEnd].every(Number.isFinite)) { + return null; + } + const intersection = Math.max(0, Math.min(end, expectedEnd) - Math.max(start, expectedStart)); + const union = Math.max(end, expectedEnd) - Math.min(start, expectedStart); + return union > 0 ? intersection / union : 0; +} + +function eventCoverage(start, end, expectedStart, expectedEnd, targetChunkSeconds) { + if (![start, end, expectedStart, expectedEnd, targetChunkSeconds].every(Number.isFinite)) { + return null; + } + const intersection = Math.max(0, Math.min(end, expectedEnd) - Math.max(start, expectedStart)); + const usefulDuration = Math.min(expectedEnd - expectedStart, targetChunkSeconds); + return usefulDuration > 0 ? Math.min(1, intersection / usefulDuration) : null; +} + +export function assertionReason(grading, metric) { + const component = (Array.isArray(grading?.componentResults) ? grading.componentResults : []) + .find((result) => result?.assertion?.metric === metric); + return typeof component?.reason === 'string' ? component.reason : ''; +} + +function outputCandidates(output) { + if (Array.isArray(output?.candidates)) { + return output.candidates; + } + if (output && ('start_seconds' in output || 'end_seconds' in output)) { + return [output]; + } + return []; +} + +function signed(value, digits = 3) { + if (!Number.isFinite(value)) { + return 'n/a'; + } + return `${value >= 0 ? '+' : ''}${value.toFixed(digits)}`; +} + +function signedMoney(value) { + if (!Number.isFinite(value)) { + return 'n/a'; + } + return `${value >= 0 ? '+' : '-'}$${Math.abs(value).toFixed(6)}`; +} + +function signedSeconds(value) { + return Number.isFinite(value) ? `${signed(value)}s` : 'n/a'; +} + +export function summarizeResults(results) { + const conditions = [ + ...CONDITION_ORDER, + ...new Set(results.map((result) => result.condition).filter( + (condition) => !CONDITION_ORDER.includes(condition), + )), + ]; + return conditions.map((condition) => { + const selected = results.filter((result) => result.condition === condition); + const valid = selected.filter((result) => result.integrityPassed === true); + const scored = valid.filter((result) => Number.isFinite(result.chunkHit)); + return { + condition, + runs: selected.length, + passed: selected.filter((result) => result.success).length, + integrityPassed: valid.length, + chunkHits: scored.filter((result) => result.chunkHit === 1).length, + top1ChunkHits: scored.filter((result) => ( + (Number.isFinite(result.top1ChunkHit) ? result.top1ChunkHit : result.chunkHit) === 1 + )).length, + chunkScored: scored.length, + rawChunkHits: selected.filter((result) => result.chunkHit === 1).length, + rawChunkScored: selected.filter((result) => Number.isFinite(result.chunkHit)).length, + chunkHitRate: mean(scored.map((result) => result.chunkHit)), + top1ChunkHitRate: mean(scored.map((result) => ( + Number.isFinite(result.top1ChunkHit) ? result.top1ChunkHit : result.chunkHit + ))), + meanChunkMrr: mean(scored.map((result) => ( + Number.isFinite(result.chunkMrr) ? result.chunkMrr : result.chunkHit + ))), + meanCandidateCount: mean(scored.map((result) => ( + Number.isFinite(result.candidateCount) ? result.candidateCount : 1 + ))), + meanEventCoverage: mean(scored.map((result) => result.eventCoverage)), + durationInRangeRate: mean(scored.map((result) => result.durationInRange)), + meanIou: mean(scored.map((result) => result.iou)), + meanBestIou: mean(scored.map((result) => ( + Number.isFinite(result.bestIou) ? result.bestIou : result.iou + ))), + recall03: mean(scored.map((result) => result.recall03)), + recall05: mean(scored.map((result) => result.recall05)), + recall07: mean(scored.map((result) => result.recall07)), + recallAt3_03: mean(scored.map((result) => ( + Number.isFinite(result.recallAt3_03) ? result.recallAt3_03 : result.recall03 + ))), + recallAt3_05: mean(scored.map((result) => ( + Number.isFinite(result.recallAt3_05) ? result.recallAt3_05 : result.recall05 + ))), + recallAt3_07: mean(scored.map((result) => ( + Number.isFinite(result.recallAt3_07) ? result.recallAt3_07 : result.recall07 + ))), + meanStartError: mean(scored.map((result) => ( + absolute(boundaryError(result.predictedStart, result.expectedStart)) + ))), + meanEndError: mean(scored.map((result) => ( + absolute(boundaryError(result.predictedEnd, result.expectedEnd)) + ))), + meanDurationError: mean(scored.map((result) => absolute(durationError(result)))), + meanLatencyMs: mean(selected.map((result) => result.latencyMs)), + totalLatencyMs: sum(selected.map((result) => result.latencyMs)), + meanTotalTokens: mean(selected.map((result) => result.totalTokens)), + totalTokens: sumOrNull(selected.map((result) => result.totalTokens)), + meanPromptTokens: mean(selected.map((result) => result.promptTokens)), + promptTokens: sumOrNull(selected.map((result) => result.promptTokens)), + meanUncachedPromptTokens: mean(selected.map((result) => ( + tokenDifference(result.promptTokens, result.cachedTokens) + ))), + uncachedPromptTokens: sumOrNull(selected.map((result) => ( + tokenDifference(result.promptTokens, result.cachedTokens) + ))), + meanCachedTokens: mean(selected.map((result) => result.cachedTokens)), + cachedTokens: sumOrNull(selected.map((result) => result.cachedTokens)), + meanCompletionTokens: mean(selected.map((result) => result.completionTokens)), + completionTokens: sumOrNull(selected.map((result) => result.completionTokens)), + meanReasoningTokens: mean(selected.map((result) => result.reasoningTokens)), + reasoningTokens: sumOrNull(selected.map((result) => result.reasoningTokens)), + requests: sumOrNull(selected.map((result) => result.requests)), + meanCost: mean(selected.map((result) => result.cost)), + cost: sumOrNull(selected.map((result) => result.cost)), + modelTurns: sum(selected.map((result) => result.modelTurns)), + agentItems: sum(selected.map((result) => result.agentItems)), + toolCalls: sum(selected.map((result) => result.toolCalls)), + mcpCalls: sum(selected.map((result) => result.mcpCalls)), + shellCalls: sum(selected.map((result) => result.shellCalls)), + skillLoads: sum(selected.map((result) => result.skillLoads)), + }; + }).filter((summary) => summary.runs > 0); +} + +export function summarizePrimaryPairs(results) { + const byCondition = new Map(CONDITION_ORDER.slice(0, 2).map((condition) => [condition, new Map()])); + for (const result of results) { + const selected = byCondition.get(result.condition); + if (selected) { + selected.set(`${result.task}\u0000${result.repetition}`, result); + } + } + const on = byCondition.get('vidxp-on'); + const off = byCondition.get('vidxp-off'); + const keys = new Set([...on.keys(), ...off.keys()]); + const pairs = [...keys].map((key) => ({ on: on.get(key), off: off.get(key) })); + const valid = pairs.filter(({ on: onResult, off: offResult }) => ( + onResult?.integrityPassed === true + && offResult?.integrityPassed === true + && Number.isFinite(onResult?.chunkHit) + && Number.isFinite(offResult?.chunkHit) + && Number.isFinite(onResult?.totalTokens) + && Number.isFinite(offResult?.totalTokens) + )); + return { + totalPairs: pairs.length, + validPairs: valid.length, + pairs: valid, + results: valid.flatMap(({ on: onResult, off: offResult }) => [onResult, offResult]), + }; +} + +function deterministicRescore(results, evaluationId) { + const python = process.env.PROMPTFOO_PYTHON || 'python3'; + const script = fileURLToPath(new URL('./rescore_eval.py', import.meta.url)); + const manifest = parseJson(readFileSync( + fileURLToPath(new URL('../tasks/longvale-part9-pilot.json', import.meta.url)), + 'utf8', + ), []); + const durationByTask = new Map(manifest.map((task) => [task.id, task.duration_seconds])); + const input = results.map((result) => ({ + test_idx: result.testIdx, + output: result.outputText, + vars: { + ...result.testVars, + duration_seconds: durationByTask.get(result.task) ?? result.testVars.duration_seconds, + }, + metadata: { evaluationId, ...result.providerMetadata }, + spans: result.traceSpans, + })); + const completed = spawnSync(python, [script], { + encoding: 'utf8', + env: process.env, + input: JSON.stringify(input), + maxBuffer: 16 * 1024 * 1024, + }); + if (completed.status !== 0) { + throw new Error(completed.stderr.trim() || 'deterministic rescore failed'); + } + const byIndex = new Map(parseJson(completed.stdout, []).map((item) => [item.test_idx, item])); + for (const result of results) { + const audit = byIndex.get(result.testIdx); + if (!audit) { + throw new Error(`deterministic rescore omitted test ${result.testIdx}`); + } + const named = audit.temporal?.namedScores || {}; + result.integrityPassed = audit.boundary?.pass === true; + result.integrityReason = audit.boundary?.reason || ''; + result.qualityReason = audit.temporal?.reason || ''; + result.chunkHit = Number.isFinite(named.bounded_chunk_hit) + ? named.bounded_chunk_hit : null; + result.top1ChunkHit = Number.isFinite(named.bounded_chunk_hit_at_1) + ? named.bounded_chunk_hit_at_1 : result.chunkHit; + result.chunkMrr = Number.isFinite(named.bounded_chunk_mrr) + ? named.bounded_chunk_mrr : result.chunkHit; + result.candidateCount = Number.isFinite(named.candidate_count) + ? named.candidate_count : result.candidateCount; + result.eventCoverage = Number.isFinite(named.event_coverage) + ? named.event_coverage : null; + result.durationInRange = Number.isFinite(named.chunk_duration_in_range) + ? named.chunk_duration_in_range : null; + result.iou = Number.isFinite(named.temporal_iou) ? named.temporal_iou : null; + result.bestIou = Number.isFinite(named.best_temporal_iou) + ? named.best_temporal_iou : result.iou; + result.recall03 = Number.isFinite(named.r1_tiou_0_3) ? named.r1_tiou_0_3 : null; + result.recall05 = Number.isFinite(named.r1_tiou_0_5) ? named.r1_tiou_0_5 : null; + result.recall07 = Number.isFinite(named.r1_tiou_0_7) ? named.r1_tiou_0_7 : null; + result.recallAt3_03 = Number.isFinite(named.r3_tiou_0_3) + ? named.r3_tiou_0_3 : result.recall03; + result.recallAt3_05 = Number.isFinite(named.r3_tiou_0_5) + ? named.r3_tiou_0_5 : result.recall05; + result.recallAt3_07 = Number.isFinite(named.r3_tiou_0_7) + ? named.r3_tiou_0_7 : result.recall07; + } +} + +export function loadLatestEvaluation({ rescore = false } = {}) { + const configDirectory = process.env.PROMPTFOO_CONFIG_DIR || join(homedir(), '.promptfoo'); + const databasePath = join(configDirectory, 'promptfoo.db'); + const database = new DatabaseSync(databasePath, { readOnly: true }); + try { + const evaluation = database.prepare( + 'SELECT id, created_at, description FROM evals ORDER BY created_at DESC LIMIT 1', + ).get(); + if (!evaluation) { + throw new Error('Promptfoo has no saved evaluation.'); + } + const rows = database.prepare(` + SELECT id, test_idx, test_case, response, success, score, latency_ms, cost, + error, grading_result, named_scores + FROM eval_results + WHERE eval_id = ? + ORDER BY test_idx, id + `).all(evaluation.id); + const traceRows = database.prepare(` + SELECT trace_id, metadata + FROM traces + WHERE evaluation_id = ? + `).all(evaluation.id); + const spansForTrace = database.prepare(` + SELECT name, start_time, end_time, attributes + FROM spans + WHERE trace_id = ? + ORDER BY start_time + `); + const traceStats = new Map(); + let firstSpan = null; + let lastSpan = null; + for (const trace of traceRows) { + const metadata = parseJson(trace.metadata); + const spans = spansForTrace.all(trace.trace_id); + const itemIds = new Set(); + let agentItems = 0; + let toolCalls = 0; + let mcpCalls = 0; + let shellCalls = 0; + for (const span of spans) { + const attributes = parseJson(span.attributes); + const itemId = attributes['codex.item.id']; + const itemType = attributes['codex.item.type']; + if (typeof itemId === 'string' && !itemIds.has(itemId)) { + itemIds.add(itemId); + agentItems += 1; + if (itemType === 'command_execution') { + shellCalls += 1; + toolCalls += 1; + } else if (typeof itemType === 'string' && itemType.endsWith('_tool_call')) { + toolCalls += 1; + } + if (itemType === 'mcp_tool_call' && attributes['codex.mcp.server'] === 'vidxp') { + mcpCalls += 1; + } + } + if (Number.isFinite(span.start_time)) { + firstSpan = firstSpan === null ? span.start_time : Math.min(firstSpan, span.start_time); + } + if (Number.isFinite(span.end_time)) { + lastSpan = lastSpan === null ? span.end_time : Math.max(lastSpan, span.end_time); + } + } + traceStats.set(metadata.testIdx, { + agentItems, + toolCalls, + mcpCalls, + shellCalls, + spans: spans.map((span) => ({ + name: span.name, + start_time: span.start_time, + end_time: span.end_time, + attributes: parseJson(span.attributes), + })), + }); + } + + const results = rows.map((row) => { + const testCase = parseJson(row.test_case); + const response = parseJson(row.response); + const output = parseJson(response.output); + const candidates = outputCandidates(output); + const topCandidate = candidates[0] || {}; + const namedScores = parseJson(row.named_scores); + const grading = parseJson(row.grading_result); + const responseMetadata = response.metadata || {}; + const stats = traceStats.get(row.test_idx) || {}; + const recordedItems = summarizeRecordedItems(response.raw) || stats; + const rolloutTurns = rolloutModelTurns( + testCase.vars?.condition || 'unknown', + response.sessionId, + ); + const modelTurns = Number.isFinite(rolloutTurns) + ? rolloutTurns + : (Number.isFinite(responseMetadata.modelTurns) ? responseMetadata.modelTurns : 0); + return { + task: testCase.metadata?.task_id || testCase.vars?.id || String(row.test_idx), + machineId: testCase.metadata?.machine_id || process.env.VIDXP_EVAL_MACHINE_ID, + condition: testCase.vars?.condition || 'unknown', + expectedVidxp: testCase.vars?.expected_vidxp === true, + evaluationMode: testCase.vars?.evaluation_mode + || testCase.metadata?.evaluation_mode + || 'unknown', + repetition: testCase.vars?.repetition || testCase.metadata?.repetition || 1, + testIdx: row.test_idx, + testVars: testCase.vars || {}, + providerMetadata: responseMetadata, + routedModalities: Array.isArray(responseMetadata.selectedModalities) + ? responseMetadata.selectedModalities + : [], + searchQuery: typeof responseMetadata.searchQuery === 'string' + ? responseMetadata.searchQuery + : null, + candidateTopK: Number.isFinite(responseMetadata.candidateTopK) + ? responseMetadata.candidateTopK + : null, + instructionProfile: responseMetadata.instructionProfile || null, + outputText: typeof response.output === 'string' ? response.output : '', + traceSpans: stats.spans || [], + success: row.success === 1, + reason: grading.reason || row.error || '', + integrityPassed: namedScores.ablation_boundary === 1, + integrityReason: namedScores.ablation_boundary === 1 + ? '' + : (assertionReason(grading, 'ablation_boundary') || row.error || grading.reason || ''), + qualityReason: assertionReason(grading, 'temporal_grounding') + || row.error || grading.reason || '', + expectedStart: testCase.vars?.expected_start, + expectedEnd: testCase.vars?.expected_end, + predictedStart: topCandidate.start_seconds, + predictedEnd: topCandidate.end_seconds, + answer: output.answer, + modalities: Array.isArray(topCandidate.modalities) ? topCandidate.modalities : [], + sourceJobId: output.source_job_id, + evidenceCount: candidates.reduce( + (count, candidate) => count + ( + Array.isArray(candidate?.evidence_ids) + ? candidate.evidence_ids.length + : (Array.isArray(candidate?.evidence) ? candidate.evidence.length : 0) + ), + 0, + ), + candidateCount: candidates.length, + rankedCandidates: Array.isArray(output.candidates), + chunkHit: Number.isFinite(namedScores.bounded_chunk_hit) + ? namedScores.bounded_chunk_hit + : null, + top1ChunkHit: Number.isFinite(namedScores.bounded_chunk_hit_at_1) + ? namedScores.bounded_chunk_hit_at_1 + : (Number.isFinite(namedScores.bounded_chunk_hit) + ? namedScores.bounded_chunk_hit : null), + chunkMrr: Number.isFinite(namedScores.bounded_chunk_mrr) + ? namedScores.bounded_chunk_mrr + : (Number.isFinite(namedScores.bounded_chunk_hit) + ? namedScores.bounded_chunk_hit : null), + eventCoverage: Number.isFinite(namedScores.event_coverage) + ? namedScores.event_coverage + : null, + durationInRange: Number.isFinite(namedScores.chunk_duration_in_range) + ? namedScores.chunk_duration_in_range + : null, + iou: Number.isFinite(namedScores.temporal_iou) ? namedScores.temporal_iou : null, + bestIou: Number.isFinite(namedScores.best_temporal_iou) + ? namedScores.best_temporal_iou + : (Number.isFinite(namedScores.temporal_iou) ? namedScores.temporal_iou : null), + recall03: Number.isFinite(namedScores.r1_tiou_0_3) + ? namedScores.r1_tiou_0_3 + : null, + recall05: Number.isFinite(namedScores.r1_tiou_0_5) + ? namedScores.r1_tiou_0_5 + : null, + recall07: Number.isFinite(namedScores.r1_tiou_0_7) + ? namedScores.r1_tiou_0_7 + : null, + recallAt3_03: Number.isFinite(namedScores.r3_tiou_0_3) + ? namedScores.r3_tiou_0_3 + : (Number.isFinite(namedScores.r1_tiou_0_3) + ? namedScores.r1_tiou_0_3 : null), + recallAt3_05: Number.isFinite(namedScores.r3_tiou_0_5) + ? namedScores.r3_tiou_0_5 + : (Number.isFinite(namedScores.r1_tiou_0_5) + ? namedScores.r1_tiou_0_5 : null), + recallAt3_07: Number.isFinite(namedScores.r3_tiou_0_7) + ? namedScores.r3_tiou_0_7 + : (Number.isFinite(namedScores.r1_tiou_0_7) + ? namedScores.r1_tiou_0_7 : null), + latencyMs: row.latency_ms, + totalTokens: response.tokenUsage?.total, + promptTokens: response.tokenUsage?.prompt, + cachedTokens: response.tokenUsage?.cached, + completionTokens: response.tokenUsage?.completion, + reasoningTokens: response.tokenUsage?.completionDetails?.reasoning, + requests: response.tokenUsage?.numRequests, + cost: row.cost, + modelTurns, + agentItems: recordedItems.agentItems || 0, + toolCalls: recordedItems.toolCalls || 0, + mcpCalls: recordedItems.mcpCalls || 0, + shellCalls: recordedItems.shellCalls || 0, + skillLoads: Array.isArray(responseMetadata.skillCalls) + ? responseMetadata.skillCalls.length + : 0, + }; + }); + if (rescore) { + deterministicRescore(results, evaluation.id); + } + return { + ...evaluation, + results, + mode: (() => { + const modes = new Set(results.map((result) => result.evaluationMode)); + return modes.size === 1 ? [...modes][0] : 'unknown'; + })(), + wallTimeMs: firstSpan === null || lastSpan === null ? null : lastSpan - firstSpan, + machineId: (() => { + const ids = new Set(results.map((result) => result.machineId).filter(Boolean)); + return ids.size === 1 ? [...ids][0] : 'unknown'; + })(), + rescored: rescore, + }; + } finally { + database.close(); + } +} + +export function summarizeRetrieval(result, trace) { + const moments = (Array.isArray(trace?.moments) ? trace.moments : []) + .slice() + .sort((left, right) => (left?.rank ?? Infinity) - (right?.rank ?? Infinity)); + const topMoment = moments.find((moment) => moment?.rank === 1) || moments[0]; + const targetChunkSeconds = Number(result.testVars?.target_chunk_seconds) > 0 + ? Number(result.testVars.target_chunk_seconds) + : 10; + const minEventCoverage = Number(result.testVars?.min_event_coverage) > 0 + ? Number(result.testVars.min_event_coverage) + : 0.5; + const surfaceCandidates = (Array.isArray(trace?.surface_candidates) + ? trace.surface_candidates : []) + .filter((candidate) => candidate?.state === 'ready') + .slice() + .sort((left, right) => (left?.rank ?? Infinity) - (right?.rank ?? Infinity)); + const bestByModality = new Map(); + for (const moment of moments) { + for (const hit of Array.isArray(moment?.hits) ? moment.hits : []) { + const iou = intervalIou( + hit.start, + hit.end, + result.expectedStart, + result.expectedEnd, + ); + const current = bestByModality.get(hit.modality); + if (current === undefined || (iou ?? -1) > (current.iou ?? -1)) { + bestByModality.set(hit.modality, { + ...hit, + iou, + fusedRank: moment.rank, + fusedStart: moment.start, + fusedEnd: moment.end, + }); + } + } + } + return { + task: result.task, + repetition: result.repetition, + condition: result.condition, + finalChunkHit: result.chunkHit, + expectedStart: result.expectedStart, + expectedEnd: result.expectedEnd, + topMoment, + topMomentIou: topMoment + ? intervalIou( + topMoment.start, + topMoment.end, + result.expectedStart, + result.expectedEnd, + ) + : null, + momentIous: moments.map((moment) => intervalIou( + moment.start, + moment.end, + result.expectedStart, + result.expectedEnd, + )), + surfaceCandidates, + surfaceRanks: surfaceCandidates.map((candidate, index) => ( + Number.isFinite(candidate.rank) ? candidate.rank : index + 1 + )), + surfaceCoverages: surfaceCandidates.map((candidate) => eventCoverage( + candidate.start, + candidate.end, + result.expectedStart, + result.expectedEnd, + targetChunkSeconds, + )), + minEventCoverage, + bestByModality, + }; +} + +export function summarizeSurfaceRecall(retrievals, depth) { + const scored = retrievals.filter((retrieval) => retrieval.surfaceCoverages.some(Number.isFinite)); + const bestCoverages = scored.map((retrieval) => { + const candidates = retrieval.surfaceCoverages.filter((coverage, index) => ( + Number.isFinite(coverage) && retrieval.surfaceRanks[index] <= depth + )); + return candidates.length > 0 ? Math.max(...candidates) : 0; + }); + const hits = bestCoverages.filter((coverage, index) => ( + coverage >= scored[index].minEventCoverage + )).length; + return { + hits, + scored: scored.length, + rate: scored.length > 0 ? hits / scored.length : null, + meanBestCoverage: mean(bestCoverages), + }; +} + +export function summarizeSurfaceTransfer(retrievals, depth) { + const summary = { + surfacedAndReturned: 0, + surfacedOnly: 0, + returnedOnly: 0, + neither: 0, + }; + for (const retrieval of retrievals) { + const candidates = retrieval.surfaceCoverages.filter((coverage, index) => ( + Number.isFinite(coverage) && retrieval.surfaceRanks[index] <= depth + )); + if (candidates.length === 0 || !Number.isFinite(retrieval.finalChunkHit)) { + continue; + } + const surfaced = Math.max(...candidates) >= retrieval.minEventCoverage; + const returned = retrieval.finalChunkHit === 1; + if (surfaced && returned) summary.surfacedAndReturned += 1; + else if (surfaced) summary.surfacedOnly += 1; + else if (returned) summary.returnedOnly += 1; + else summary.neither += 1; + } + return summary; +} + +function retrievalRecallAt(retrievals, depth, threshold) { + return mean(retrievals.map((retrieval) => { + const candidates = retrieval.momentIous.slice(0, depth).filter(Number.isFinite); + return candidates.length > 0 && Math.max(...candidates) >= threshold ? 1 : 0; + })); +} + +export function loadRetrievalTraces(results) { + const jobIds = [...new Set( + results + .filter((result) => result.expectedVidxp && result.integrityPassed) + .map((result) => result.sourceJobId) + .filter((jobId) => typeof jobId === 'string' && jobId.length > 0), + )]; + if (jobIds.length === 0) { + return {}; + } + const python = process.env.PROMPTFOO_PYTHON || 'python3'; + const script = fileURLToPath(new URL('./retrieval_trace.py', import.meta.url)); + const completed = spawnSync(python, [script, ...jobIds], { + encoding: 'utf8', + env: process.env, + }); + if (completed.status !== 0) { + throw new Error(completed.stderr.trim() || 'durable retrieval trace failed'); + } + return parseJson(completed.stdout); +} + +export function renderReport( + evaluation, + { showAll = false, showResponses = false, showRetrieval = true } = {}, +) { + const summaries = summarizeResults(evaluation.results); + const created = Number.isFinite(evaluation.created_at) + ? new Date(evaluation.created_at).toISOString() + : String(evaluation.created_at); + const taskCount = new Set(evaluation.results.map((result) => result.task)).size; + const isSmoke = evaluation.mode === 'smoke' + || (evaluation.mode === 'unknown' && taskCount === 1); + const runType = isSmoke ? 'development smoke' : evaluation.mode; + const rankedCandidates = evaluation.results.some((result) => result.rankedCandidates); + const primaryPairs = summarizePrimaryPairs(evaluation.results); + const pairedSummaries = summarizeResults(primaryPairs.results); + console.log(`\nEvaluation comparison: ${evaluation.id}`); + console.log( + `Run type: ${runType} | machine: ${evaluation.machineId || 'unknown'} ` + + `| created: ${created} | wall time: ${seconds(evaluation.wallTimeMs)}`, + ); + const localResults = evaluation.results.filter( + (result) => result.condition === 'local-slm' + || result.condition === 'local-slm-planner', + ); + if (localResults.length > 0) { + const models = new Set(localResults.map((result) => { + const model = result.providerMetadata?.model; + return model?.provider && model?.model ? `${model.provider}/${model.model}` : null; + }).filter(Boolean)); + const coldStarts = localResults.filter( + (result) => result.providerMetadata?.coldStart === true, + ).length; + console.log( + `Local SLM: ${models.size === 1 ? [...models][0] : 'unknown'} | ` + + `managed runtime cold starts: ${coldStarts}/${localResults.length}`, + ); + } + const passedAssertions = evaluation.results.filter((result) => result.success).length; + console.log( + `${evaluation.rescored ? 'Original at-run Promptfoo assertions' : 'Stored Promptfoo assertions'}: ` + + `${passedAssertions === evaluation.results.length ? 'PASS' : 'FAIL'}` + + ` (${passedAssertions}/${evaluation.results.length} runs passed every at-run assertion)`, + ); + if (evaluation.rescored) { + console.log( + 'Current deterministic audit: saved responses and traces rescored against the current ' + + 'scorer and validated media durations; no agent or model calls made.', + ); + } + console.log('Product outcome:'); + console.table(summaries.map((summary) => ({ + condition: summary.condition, + runs: summary.runs, + integrity: `${summary.integrityPassed}/${summary.runs}`, + scorable: `${summary.chunkScored}/${summary.runs}`, + [rankedCandidates ? 'valid hit@3' : 'valid hits']: summary.chunkScored + ? `${summary.chunkHits}/${summary.chunkScored}` + : 'n/a', + ...(rankedCandidates ? { + 'valid hit@1': summary.chunkScored + ? `${summary.top1ChunkHits}/${summary.chunkScored}` + : 'n/a', + MRR: fixed(summary.meanChunkMrr, 3), + candidates: fixed(summary.meanCandidateCount, 2), + } : {}), + 'all output hits': summary.rawChunkScored + ? `${summary.rawChunkHits}/${summary.rawChunkScored}` + : 'n/a', + [rankedCandidates ? 'hit@3 rate' : 'hit rate']: fixed(summary.chunkHitRate, 3), + coverage: fixed(summary.meanEventCoverage, 3), + 'duration valid': fixed(summary.durationInRangeRate, 3), + 'avg time': seconds(summary.meanLatencyMs), + 'total time': seconds(summary.totalLatencyMs), + }))); + console.log( + ` Primary quality: ${rankedCandidates ? 'at least one of up to three ordered ' : 'one '}` + + '8ā€“12s clip covers at least half of the event available to a 10s clip. ' + + 'Quality rates exclude runs that violated their condition. Time, tokens, and activity include ' + + 'all runs. Boundary IoU and R@ thresholds remain secondary diagnostics.', + ); + console.log('Boundary diagnostics (secondary):'); + console.table(summaries.map((summary) => ({ + condition: summary.condition, + 'top-1 IoU': fixed(summary.meanIou, 4), + ...(rankedCandidates ? { 'best@3 IoU': fixed(summary.meanBestIou, 4) } : {}), + 'R1@.3': fixed(summary.recall03, 3), + 'R1@.5': fixed(summary.recall05, 3), + 'R1@.7': fixed(summary.recall07, 3), + ...(rankedCandidates ? { + 'R3@.3': fixed(summary.recallAt3_03, 3), + 'R3@.5': fixed(summary.recallAt3_05, 3), + 'R3@.7': fixed(summary.recallAt3_07, 3), + } : {}), + 'start MAE': secondsValue(summary.meanStartError), + 'end MAE': secondsValue(summary.meanEndError), + 'duration MAE': secondsValue(summary.meanDurationError), + }))); + console.log('Token usage and Promptfoo cost:'); + console.table(summaries.map((summary) => ({ + condition: summary.condition, + runs: summary.runs, + 'avg total': integer(summary.meanTotalTokens), + 'avg input': integer(summary.meanPromptTokens), + 'avg cached': integer(summary.meanCachedTokens), + 'avg uncached': integer(summary.meanUncachedPromptTokens), + 'avg output': integer(summary.meanCompletionTokens), + 'avg reasoning': integer(summary.meanReasoningTokens), + 'all tokens': integer(summary.totalTokens), + 'avg cost': money(summary.meanCost), + 'all cost': money(summary.cost), + }))); + console.log( + ' Reasoning tokens are included in output tokens. Cost is Promptfoo\'s supplied provider ' + + 'estimate, kept unchanged as a consistent comparison metric; it is not an end-user bill or ' + + 'a verified Codex-plan charge. The local provider reports zero external provider charge; ' + + 'local compute is not priced.', + ); + console.log('Agent activity:'); + console.table(summaries.map((summary) => ({ + condition: summary.condition, + 'model requests': summary.requests, + turns: summary.modelTurns, + items: summary.agentItems, + 'tool calls': summary.toolCalls, + MCP: summary.mcpCalls, + shell: summary.shellCalls, + skill: summary.skillLoads, + }))); + console.log( + ' Items and tool-type counts come from Promptfoo\'s saved provider response. Model turns ' + + 'come from Codex rollout events or the local provider\'s reported request count.', + ); + + const on = summaries.find((summary) => summary.condition === 'vidxp-on'); + const off = summaries.find((summary) => summary.condition === 'vidxp-off'); + const cleanUser = summaries.find((summary) => summary.condition === 'clean-user'); + const pairedOn = pairedSummaries.find((summary) => summary.condition === 'vidxp-on'); + const pairedOff = pairedSummaries.find((summary) => summary.condition === 'vidxp-off'); + if (on && off && pairedOn && pairedOff) { + const latencyDelta = pairedOn.meanLatencyMs - pairedOff.meanLatencyMs; + const latencyPercent = pairedOff.meanLatencyMs + ? Math.abs(latencyDelta) / pairedOff.meanLatencyMs * 100 + : null; + const tokenDelta = Number.isFinite(pairedOn.meanTotalTokens) + && Number.isFinite(pairedOff.meanTotalTokens) + ? pairedOn.meanTotalTokens - pairedOff.meanTotalTokens + : null; + const tokenPercent = Number.isFinite(tokenDelta) && pairedOff.meanTotalTokens + ? Math.abs(tokenDelta) / pairedOff.meanTotalTokens * 100 + : null; + const uncachedDelta = Number.isFinite(pairedOn.meanUncachedPromptTokens) + && Number.isFinite(pairedOff.meanUncachedPromptTokens) + ? pairedOn.meanUncachedPromptTokens - pairedOff.meanUncachedPromptTokens + : null; + console.log( + `Matched condition-valid, scorable VidXP-on minus VidXP-off (${primaryPairs.validPairs}` + + `/${primaryPairs.totalPairs} pairs):`, + ); + const chunkHitDelta = Number.isFinite(pairedOn.chunkHitRate) + && Number.isFinite(pairedOff.chunkHitRate) + ? pairedOn.chunkHitRate - pairedOff.chunkHitRate + : null; + console.log( + ` bounded chunk hit${rankedCandidates ? '@3' : ''} rate: ` + + signed(chunkHitDelta, 3), + ); + if (rankedCandidates) { + console.log( + ` bounded chunk hit@1 rate: ` + + signed(pairedOn.top1ChunkHitRate - pairedOff.top1ChunkHitRate, 3), + ); + console.log( + ` bounded chunk MRR: ` + + signed(pairedOn.meanChunkMrr - pairedOff.meanChunkMrr, 3), + ); + } + console.log(` top-1 mean IoU: ${signed(pairedOn.meanIou - pairedOff.meanIou, 4)}`); + if (rankedCandidates) { + console.log( + ` best@3 mean IoU: ${signed(pairedOn.meanBestIou - pairedOff.meanBestIou, 4)}`, + ); + } + console.log( + ` average latency: ${signed(latencyDelta / 1000, 3)}s` + + (Number.isFinite(latencyPercent) + ? ` (${latencyPercent.toFixed(1)}% ${latencyDelta <= 0 ? 'faster' : 'slower'})` + : ''), + ); + console.log( + ` average tokens: ${Number.isFinite(tokenDelta) && tokenDelta >= 0 ? '+' : ''}${integer(tokenDelta)}` + + (Number.isFinite(tokenPercent) + ? ` (${tokenPercent.toFixed(1)}% ${tokenDelta <= 0 ? 'fewer' : 'more'})` + : ''), + ); + console.log( + ` average uncached input tokens: ${Number.isFinite(uncachedDelta) && uncachedDelta >= 0 ? '+' : ''}` + + integer(uncachedDelta), + ); + const costDelta = Number.isFinite(pairedOn.meanCost) && Number.isFinite(pairedOff.meanCost) + ? pairedOn.meanCost - pairedOff.meanCost + : null; + console.log(` average Promptfoo cost: ${signedMoney(costDelta)}`); + const latencyWins = primaryPairs.pairs.filter(({ on: onResult, off: offResult }) => ( + Number.isFinite(onResult.latencyMs) + && Number.isFinite(offResult.latencyMs) + && onResult.latencyMs < offResult.latencyMs + )).length; + const tokenWins = primaryPairs.pairs.filter(({ on: onResult, off: offResult }) => ( + onResult.totalTokens < offResult.totalTokens + )).length; + const costWins = primaryPairs.pairs.filter(({ on: onResult, off: offResult }) => ( + Number.isFinite(onResult.cost) + && Number.isFinite(offResult.cost) + && onResult.cost < offResult.cost + )).length; + console.log( + ` pairwise efficiency wins: faster ${latencyWins}/${primaryPairs.validPairs}; ` + + `fewer tokens ${tokenWins}/${primaryPairs.validPairs}; lower Promptfoo cost ` + + `${costWins}/${primaryPairs.validPairs}`, + ); + if (evaluation.mode === 'pilot') { + const integrityComplete = primaryPairs.validPairs === primaryPairs.totalPairs + && primaryPairs.totalPairs === on.runs + && primaryPairs.totalPairs === off.runs; + const productGateAvailable = integrityComplete + && Number.isFinite(chunkHitDelta) + && Number.isFinite(tokenDelta); + const productGatePassed = productGateAvailable && chunkHitDelta >= 0 && tokenDelta < 0; + console.log( + ` product gate: ${productGateAvailable + ? (productGatePassed ? 'PASS' : 'FAIL') + : 'NOT SCORED (incomplete valid/scorable pairs)'}` + + ` (VidXP must match or improve bounded-chunk hit${rankedCandidates ? '@3' : ''} ` + + 'rate and use fewer total tokens)', + ); + } else { + console.log(' product gate: NOT SCORED (development smoke)'); + } + } + + if (cleanUser) { + console.log('Clean-user supporting comparisons:'); + console.table([off, on].filter(Boolean).map((reference) => ({ + comparison: `clean-user minus ${reference.condition}`, + [rankedCandidates ? 'hit@3 Ī”' : 'hit-rate Ī”']: + signed(cleanUser.chunkHitRate - reference.chunkHitRate, 3), + 'top-1 IoU Ī”': signed(cleanUser.meanIou - reference.meanIou, 4), + 'avg time Ī”': signedSeconds((cleanUser.meanLatencyMs - reference.meanLatencyMs) / 1000), + 'avg tokens Ī”': integer(cleanUser.meanTotalTokens - reference.meanTotalTokens), + 'avg cost Ī”': signedMoney(cleanUser.meanCost - reference.meanCost), + }))); + } + + if (evaluation.results.length <= 20 || showAll) { + console.log('Per-run product result:'); + const tasks = new Set(evaluation.results.map((result) => result.task)); + if (tasks.size === 1) { + console.log(` task: ${evaluation.results[0].task}`); + } + const repeated = evaluation.results.some((result) => result.repetition > 1); + console.table(evaluation.results.map((result) => ({ + ...(tasks.size === 1 ? {} : { task: result.task }), + ...(repeated ? { repetition: result.repetition } : {}), + condition: result.condition, + ...(localResults.length > 0 ? { + route: result.routedModalities.length > 0 + ? result.routedModalities.join(',') + : 'n/a', + 'candidate depth': Number.isFinite(result.candidateTopK) + ? result.candidateTopK + : 'default', + } : {}), + integrity: result.integrityPassed ? 'yes' : 'NO', + [rankedCandidates ? 'hit@3' : 'chunk hit']: Number.isFinite(result.chunkHit) + ? (result.chunkHit === 1 ? 'yes' : 'NO') + : 'n/a', + ...(rankedCandidates ? { + 'hit@1': Number.isFinite(result.top1ChunkHit) + ? (result.top1ChunkHit === 1 ? 'yes' : 'NO') + : 'n/a', + candidates: result.candidateCount, + } : {}), + expected: interval(result.expectedStart, result.expectedEnd), + 'top candidate': interval(result.predictedStart, result.predictedEnd), + coverage: fixed(result.eventCoverage, 3), + 'duration valid': Number.isFinite(result.durationInRange) + ? (result.durationInRange === 1 ? 'yes' : 'NO') + : 'n/a', + time: seconds(result.latencyMs), + }))); + console.log('Per-run boundary diagnostics (secondary):'); + console.table(evaluation.results.map((result) => ({ + ...(tasks.size === 1 ? {} : { task: result.task }), + condition: result.condition, + 'start Ī”': signedSeconds(boundaryError(result.predictedStart, result.expectedStart)), + 'end Ī”': signedSeconds(boundaryError(result.predictedEnd, result.expectedEnd)), + 'duration Ī”': signedSeconds(durationError(result)), + 'top-1 IoU': fixed(result.iou, 4), + ...(rankedCandidates ? { 'best@3 IoU': fixed(result.bestIou, 4) } : {}), + }))); + if (localResults.length > 0) { + console.log('Local SLM search decisions:'); + console.table(localResults.map((result) => ({ + ...(tasks.size === 1 ? {} : { task: result.task }), + ...(repeated ? { repetition: result.repetition } : {}), + condition: result.condition, + prompt: result.instructionProfile || 'n/a', + modalities: result.routedModalities.length > 0 + ? result.routedModalities.join(',') + : 'n/a', + 'candidate depth': Number.isFinite(result.candidateTopK) + ? result.candidateTopK + : 'product default', + query: result.searchQuery || 'n/a', + }))); + } + console.log('Per-run usage and tools:'); + console.table(evaluation.results.map((result) => ({ + ...(tasks.size === 1 ? {} : { task: result.task }), + condition: result.condition, + total: integer(result.totalTokens), + input: integer(result.promptTokens), + cached: integer(result.cachedTokens), + uncached: integer(tokenDifference(result.promptTokens, result.cachedTokens)), + output: integer(result.completionTokens), + reasoning: integer(result.reasoningTokens), + requests: integer(result.requests), + turns: result.modelTurns, + items: result.agentItems, + tools: result.toolCalls, + MCP: result.mcpCalls, + shell: result.shellCalls, + skill: result.skillLoads, + 'Promptfoo cost': money(result.cost), + }))); + } else { + console.log(`Per-run table omitted for ${evaluation.results.length} runs; use results --all to print it.`); + } + + const failures = evaluation.results.filter((result) => result.integrityPassed === false); + if (failures.length > 0) { + console.log('Condition-integrity exclusions:'); + for (const failure of failures) { + console.log( + ` ${failure.task} repetition ${failure.repetition} [${failure.condition}]: ` + + failure.integrityReason, + ); + } + } + const unscorable = evaluation.results.filter((result) => ( + result.integrityPassed === true && !Number.isFinite(result.chunkHit) + )); + if (unscorable.length > 0) { + console.log('Condition-valid but unscorable outputs:'); + for (const failure of unscorable) { + console.log( + ` ${failure.task} repetition ${failure.repetition} [${failure.condition}]: ` + + failure.qualityReason, + ); + } + } + + if (showResponses) { + console.log('Responses:'); + for (const result of evaluation.results) { + console.log(` ${result.task} [${result.condition}]`); + console.log(` answer: ${result.answer || 'n/a'}`); + console.log(` modalities: ${result.modalities.length ? result.modalities.join(', ') : 'n/a'}`); + console.log(` source job: ${result.sourceJobId || 'n/a'} | evidence items: ${result.evidenceCount}`); + } + } + + if (showRetrieval) { + const traces = loadRetrievalTraces(evaluation.results); + const retrievals = evaluation.results + .filter((result) => ( + result.expectedVidxp + && result.integrityPassed === true + && traces[result.sourceJobId] + )) + .map((result) => summarizeRetrieval(result, traces[result.sourceJobId])); + if (retrievals.length > 0) { + console.log('VidXP raw evidence-tile recall:'); + console.table(CONDITION_ORDER.filter((condition) => ( + retrievals.some((retrieval) => retrieval.condition === condition) + )).map((condition) => { + const selected = retrievals.filter((retrieval) => retrieval.condition === condition); + const at1 = summarizeSurfaceRecall(selected, 1); + const at3 = summarizeSurfaceRecall(selected, 3); + return { + condition, + jobs: at3.scored, + 'hit@1': `${at1.hits}/${at1.scored}`, + 'hit@1 rate': fixed(at1.rate, 3), + 'hit@3': `${at3.hits}/${at3.scored}`, + 'hit@3 rate': fixed(at3.rate, 3), + 'coverage@3': fixed(at3.meanBestCoverage, 3), + }; + })); + const transfer = summarizeSurfaceTransfer(retrievals, 3); + console.log( + ` Raw top-three evidence to final answer: ${transfer.surfacedAndReturned} raw-tile hit ` + + `and returned hit; ${transfer.surfacedOnly} raw-tile hit only; ${transfer.returnedOnly} ` + + `returned-window hit only; ${transfer.neither} neither.`, + ); + console.log( + ' This VidXP-only diagnostic scores the original ready tile boundaries exposed by ' + + 'get_job_evidence. The final-answer gate scores delivered 8ā€“12s windows, so expanding ' + + 'a point or short span into its 10s serving window can produce a returned-window hit ' + + 'when the original tile is below the raw-boundary coverage threshold.', + ); + } + console.log('VidXP retrieval boundaries:'); + console.table(retrievals.map((retrieval) => ({ + task: retrieval.task, + condition: retrieval.condition, + expected: interval(retrieval.expectedStart, retrieval.expectedEnd), + 'top fused': interval(retrieval.topMoment?.start, retrieval.topMoment?.end), + 'fused IoU': fixed(retrieval.topMomentIou, 4), + modalities: Array.isArray(retrieval.topMoment?.modalities) + ? retrieval.topMoment.modalities.join(', ') + : 'n/a', + hits: Array.isArray(retrieval.topMoment?.hits) ? retrieval.topMoment.hits.length : 0, + }))); + console.log('VidXP fused retrieval recall:'); + console.table(CONDITION_ORDER.filter((condition) => ( + retrievals.some((retrieval) => retrieval.condition === condition) + )).flatMap((condition) => { + const selected = retrievals.filter((retrieval) => retrieval.condition === condition); + return [0.3, 0.5, 0.7].map((threshold) => ({ + condition, + threshold, + 'R@1': fixed(retrievalRecallAt(selected, 1, threshold), 3), + 'R@3': fixed(retrievalRecallAt(selected, 3, threshold), 3), + 'R@5': fixed(retrievalRecallAt(selected, 5, threshold), 3), + })); + })); + console.log('Hits in the top fused interval:'); + console.table(retrievals.flatMap((retrieval) => ( + (Array.isArray(retrieval.topMoment?.hits) ? retrieval.topMoment.hits : []).map((hit) => ({ + task: retrieval.task, + modality: hit.modality, + rank: hit.rank, + interval: interval(hit.start, hit.end), + IoU: fixed(intervalIou( + hit.start, + hit.end, + retrieval.expectedStart, + retrieval.expectedEnd, + ), 4), + })) + ))); + console.log('Best retrieved individual hit per modality:'); + console.table(retrievals.flatMap((retrieval) => ( + [...retrieval.bestByModality.entries()].map(([modality, hit]) => ({ + task: retrieval.task, + modality, + 'fused rank': hit.fusedRank, + rank: hit.rank, + interval: interval(hit.start, hit.end), + IoU: fixed(hit.iou, 4), + })) + ))); + console.log( + ' Saved jobs contain hits retained in final fused moments. The report cannot recover ' + + 'modality candidates outside candidate_top_k or the final fused output. Retrieval ' + + 'R@K therefore covers only the fused moments saved by each agent-requested top_k.', + ); + } +} + +export function printLatestReport(options = {}) { + renderReport(loadLatestEvaluation({ rescore: options.rescore === true }), options); +} + +if (process.argv[1] && fileURLToPath(import.meta.url) === resolve(process.argv[1])) { + try { + printLatestReport({ + showAll: process.argv.includes('--all'), + showResponses: process.argv.includes('--responses'), + showRetrieval: !process.argv.includes('--no-retrieval'), + rescore: process.argv.includes('--rescore'), + }); + } catch (error) { + console.error(`Could not report the latest evaluation: ${error.message}`); + process.exitCode = 1; + } +} diff --git a/benchmarks/codex-mcp/scripts/report.test.mjs b/benchmarks/codex-mcp/scripts/report.test.mjs new file mode 100644 index 00000000..9c59e552 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/report.test.mjs @@ -0,0 +1,265 @@ +import assert from 'node:assert/strict'; +import { test } from 'node:test'; + +import { sanitizePromptfooExport } from './export-eval.mjs'; +import { + assertionReason, + loadRetrievalTraces, + summarizePrimaryPairs, + summarizeRecordedItems, + summarizeResults, + summarizeRetrieval, + summarizeSurfaceRecall, + summarizeSurfaceTransfer, +} from './report.mjs'; + +test('does not load untrusted job IDs from integrity-failed results', () => { + assert.deepEqual(loadRetrievalTraces([ + { + expectedVidxp: true, + integrityPassed: false, + sourceJobId: 'invented-job-id', + }, + ]), {}); +}); + +test('sanitizes a Promptfoo export without removing its audit data', () => { + const sanitized = sanitizePromptfooExport({ + metadata: { promptfooVersion: '0.122.2' }, + config: { apiKey: 'secret', workingDir: '/Users/test/repo/workspace' }, + results: { + results: [ + { + prompt: { raw: 'Find the event.' }, + response: { raw: 'large command output', sessionId: 'session-1', output: '{}' }, + }, + { + prompt: { raw: 'Find another event.' }, + response: { + output: '{}', + metadata: { agentRuntime: 'local-slm' }, + raw: { items: [{ type: 'mcp_tool_call', server: 'vidxp' }] }, + }, + }, + ], + }, + traces: [{ + spans: [{ + attributes: { + command: '/Users/test/tool --version; inspect /Users/tā€¦/truncated', + }, + }], + }], + }, { + repoRoot: '/Users/test/repo', + userHome: '/Users/test', + machineId: 'mac-fixture-01', + }); + + assert.equal(sanitized.config.apiKey, ''); + assert.equal(sanitized.config.workingDir, '/workspace'); + assert.equal(sanitized.results.results[0].prompt.raw, 'Find the event.'); + assert.equal(sanitized.results.results[0].response.output, '{}'); + assert.equal('raw' in sanitized.results.results[0].response, false); + assert.equal('sessionId' in sanitized.results.results[0].response, false); + assert.equal(sanitized.results.results[1].response.raw.items.length, 1); + assert.equal( + sanitized.traces[0].spans[0].attributes.command, + '/tool --version; inspect /truncated', + ); + assert.doesNotMatch(JSON.stringify(sanitized), /\btest\b/); + assert.equal(sanitized.metadata.vidxpExport.sanitized, true); + assert.equal(sanitized.metadata.vidxpExport.machineId, 'mac-fixture-01'); +}); + +test('summarizes comparison metrics by benchmark condition', () => { + const summaries = summarizeResults([ + { + condition: 'vidxp-on', success: true, iou: 0.75, + integrityPassed: true, + chunkHit: 1, eventCoverage: 1, durationInRange: 1, + recall03: 1, recall05: 1, recall07: 1, + expectedStart: 0, expectedEnd: 6, predictedStart: 0, predictedEnd: 8, + latencyMs: 75_000, totalTokens: 300_000, promptTokens: 298_000, + cachedTokens: 250_000, completionTokens: 2_000, reasoningTokens: 600, + requests: 1, cost: 0.8, agentItems: 9, toolCalls: 7, mcpCalls: 6, + shellCalls: 1, skillLoads: 1, + }, + { + condition: 'vidxp-off', success: true, iou: 0.88, + integrityPassed: true, + chunkHit: 1, eventCoverage: 1, durationInRange: 1, + recall03: 1, recall05: 1, recall07: 1, + expectedStart: 0, expectedEnd: 6, predictedStart: 0, predictedEnd: 6.8, + latencyMs: 112_000, totalTokens: 330_000, promptTokens: 326_400, + cachedTokens: 290_000, completionTokens: 3_600, reasoningTokens: 1_400, + requests: 1, cost: 0.81, agentItems: 12, toolCalls: 10, mcpCalls: 0, + shellCalls: 10, skillLoads: 0, + }, + { + condition: 'clean-user', success: true, iou: 0.9, + integrityPassed: true, + chunkHit: 1, eventCoverage: 1, durationInRange: 1, + recall03: 1, recall05: 1, recall07: 1, + expectedStart: 0, expectedEnd: 6, predictedStart: 0, predictedEnd: 6.5, + latencyMs: 80_000, totalTokens: 310_000, promptTokens: 307_000, + cachedTokens: 270_000, completionTokens: 3_000, reasoningTokens: 1_000, + requests: 1, cost: 0.7, agentItems: 11, toolCalls: 9, mcpCalls: 5, + shellCalls: 4, skillLoads: 1, + }, + ]); + + assert.deepEqual( + summaries.map((summary) => summary.condition), + ['vidxp-on', 'vidxp-off', 'clean-user'], + ); + assert.equal(summaries[0].meanIou, 0.75); + assert.equal(summaries[0].chunkHits, 1); + assert.equal(summaries[0].chunkScored, 1); + assert.equal(summaries[0].meanTotalTokens, 300_000); + assert.equal(summaries[0].chunkHitRate, 1); + assert.equal(summaries[0].meanEventCoverage, 1); + assert.equal(summaries[0].totalTokens, 300_000); + assert.equal(summaries[0].promptTokens, 298_000); + assert.equal(summaries[0].uncachedPromptTokens, 48_000); + assert.equal(summaries[0].reasoningTokens, 600); + assert.equal(summaries[0].meanEndError, 2); + assert.equal(summaries[0].meanDurationError, 2); + assert.equal(summaries[0].toolCalls, 7); + assert.equal(summaries[0].mcpCalls, 6); + assert.equal(summaries[1].meanLatencyMs, 112_000); + assert.equal(summaries[2].mcpCalls, 5); +}); + +test('keeps top-one and top-three candidate quality separate', () => { + const [summary] = summarizeResults([ + { + condition: 'vidxp-on', integrityPassed: true, + chunkHit: 1, top1ChunkHit: 0, chunkMrr: 0.5, candidateCount: 2, + eventCoverage: 1, durationInRange: 1, + iou: 0, bestIou: 0.6, + recall03: 0, recall05: 0, recall07: 0, + recallAt3_03: 1, recallAt3_05: 1, recallAt3_07: 0, + }, + ]); + + assert.equal(summary.chunkHitRate, 1); + assert.equal(summary.top1ChunkHitRate, 0); + assert.equal(summary.meanChunkMrr, 0.5); + assert.equal(summary.meanCandidateCount, 2); + assert.equal(summary.meanIou, 0); + assert.equal(summary.meanBestIou, 0.6); + assert.equal(summary.recallAt3_05, 1); +}); + +test('uses only matched integrity-valid primary pairs for the product comparison', () => { + const paired = summarizePrimaryPairs([ + { + task: 'one', repetition: 1, condition: 'vidxp-on', integrityPassed: true, + chunkHit: 1, totalTokens: 100, + }, + { + task: 'one', repetition: 1, condition: 'vidxp-off', integrityPassed: true, + chunkHit: 1, totalTokens: 200, + }, + { + task: 'two', repetition: 1, condition: 'vidxp-on', integrityPassed: false, + chunkHit: 1, totalTokens: 100, + }, + { + task: 'two', repetition: 1, condition: 'vidxp-off', integrityPassed: true, + chunkHit: 0, totalTokens: 200, + }, + ]); + + assert.equal(paired.totalPairs, 2); + assert.equal(paired.validPairs, 1); + assert.equal(paired.pairs.length, 1); + assert.deepEqual(paired.results.map((result) => result.task), ['one', 'one']); +}); + +test('counts Promptfoo recorded items without parsing command text', () => { + assert.deepEqual(summarizeRecordedItems(JSON.stringify({ + items: [ + { type: 'command_execution', command: '"$MEDIA_TOOL" -i video.mp4' }, + { type: 'mcp_tool_call', server: 'vidxp', tool: 'search_moments' }, + { type: 'file_change' }, + { type: 'agent_message' }, + ], + })), { + agentItems: 4, + toolCalls: 2, + mcpCalls: 1, + shellCalls: 1, + }); +}); + +test('reports fused and per-modality retrieval boundary quality', () => { + const summary = summarizeRetrieval( + { + task: 'opening', expectedStart: 0, expectedEnd: 6, + testVars: { target_chunk_seconds: 10, min_event_coverage: 0.5 }, + }, + { + surface_candidates: [ + { rank: 1, start: 20, end: 30, state: 'ready' }, + { rank: 2, start: 0, end: 10, state: 'ready' }, + { rank: 3, start: 40, end: 50, state: 'failed' }, + ], + moments: [ + { + rank: 1, + start: 0, + end: 8, + hits: [ + { modality: 'action', rank: 1, start: 0, end: 8 }, + { modality: 'scene', rank: 1, start: 1, end: 2 }, + { modality: 'scene', rank: 2, start: 1, end: 4 }, + ], + }, + { rank: 2, start: 20, end: 30, hits: [] }, + { rank: 3, start: 0, end: 6, hits: [] }, + ], + }, + ); + + assert.equal(summary.topMomentIou, 0.75); + assert.equal(summary.bestByModality.get('action').iou, 0.75); + assert.equal(summary.bestByModality.get('scene').rank, 2); + assert.equal(summary.bestByModality.get('scene').fusedRank, 1); + assert.equal(summary.bestByModality.get('scene').iou, 0.5); + assert.deepEqual(summary.momentIous, [0.75, 0, 1]); + assert.deepEqual(summary.surfaceCoverages, [0, 1]); + assert.deepEqual(summarizeSurfaceRecall([summary], 1), { + hits: 0, + scored: 1, + rate: 0, + meanBestCoverage: 0, + }); + assert.deepEqual(summarizeSurfaceRecall([summary], 3), { + hits: 1, + scored: 1, + rate: 1, + meanBestCoverage: 1, + }); + assert.deepEqual(summarizeSurfaceTransfer([ + { ...summary, finalChunkHit: 0 }, + ], 3), { + surfacedAndReturned: 0, + surfacedOnly: 1, + returnedOnly: 0, + neither: 0, + }); +}); + +test('extracts the reason for the requested Promptfoo assertion', () => { + const grading = { + reason: 'Combined failure summary', + componentResults: [ + { reason: 'Temporal miss', assertion: { metric: 'temporal_grounding' } }, + { reason: 'Isolation failure', assertion: { metric: 'ablation_boundary' } }, + ], + }; + + assert.equal(assertionReason(grading, 'ablation_boundary'), 'Isolation failure'); +}); diff --git a/benchmarks/codex-mcp/scripts/rescore_eval.py b/benchmarks/codex-mcp/scripts/rescore_eval.py new file mode 100644 index 00000000..8ada9788 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/rescore_eval.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import json +import sys +from typing import Any + +from vidxp.benchmarks.agent_ablation_score import ( + score_ablation_boundary, + score_temporal_grounding, +) + + +def main() -> None: + records: list[dict[str, Any]] = json.load(sys.stdin) + rescored = [] + for record in records: + context = { + "vars": record.get("vars", {}), + "metadata": record.get("metadata", {}), + "trace": {"spans": record.get("spans", [])}, + } + output = record.get("output", "") + rescored.append( + { + "test_idx": record.get("test_idx"), + "temporal": score_temporal_grounding(output, context), + "boundary": score_ablation_boundary(output, context), + } + ) + json.dump(rescored, sys.stdout, separators=(",", ":")) + + +if __name__ == "__main__": + main() diff --git a/benchmarks/codex-mcp/scripts/reset-workspace.mjs b/benchmarks/codex-mcp/scripts/reset-workspace.mjs new file mode 100644 index 00000000..f65e91fa --- /dev/null +++ b/benchmarks/codex-mcp/scripts/reset-workspace.mjs @@ -0,0 +1,59 @@ +import { + existsSync, + mkdirSync, + readdirSync, + rmSync, + writeFileSync, +} from 'node:fs'; +import { relative, resolve } from 'node:path'; + +const CONDITION_ENV = { + 'vidxp-on': 'VIDXP_EVAL_VIDXP_ON_WORKSPACE', + 'vidxp-off': 'VIDXP_EVAL_VIDXP_OFF_WORKSPACE', + 'clean-user': 'VIDXP_EVAL_CLEAN_USER_WORKSPACE', +}; + +function requireIsolatedWorkspace(condition, environment) { + const sharedRoot = resolve(environment.VIDXP_EVAL_WORKSPACE || ''); + const workspace = resolve(environment[CONDITION_ENV[condition]] || ''); + const child = relative(sharedRoot, workspace); + if (!child || child.startsWith('..') || resolve(sharedRoot, child) !== workspace) { + throw new Error(`Refusing to reset non-isolated ${condition} workspace: ${workspace}`); + } + if ( + !existsSync(workspace) + || (condition !== 'vidxp-on' && !existsSync(resolve(workspace, 'media'))) + ) { + throw new Error(`The ${condition} workspace is not prepared: ${workspace}`); + } + return workspace; +} + +export function resetEvaluationWorkspace(condition, environment = process.env) { + if (condition === 'local-slm' || condition === 'local-slm-planner') { + return; + } + if (!(condition in CONDITION_ENV)) { + throw new Error(`Unknown evaluation condition: ${condition}`); + } + const workspace = requireIsolatedWorkspace(condition, environment); + const preserved = new Set(condition === 'vidxp-on' ? ['.agents'] : ['media']); + for (const entry of readdirSync(workspace)) { + if (!preserved.has(entry)) { + rmSync(resolve(workspace, entry), { recursive: true, force: true }); + } + } + mkdirSync(resolve(workspace, 'tmp'), { recursive: true }); + if (condition === 'clean-user') { + mkdirSync(resolve(workspace, 'bin'), { recursive: true }); + const profile = `export PATH=${JSON.stringify(environment.VIDXP_EVAL_CLEAN_USER_PATH)}\n`; + for (const filename of ['.zshenv', '.zprofile', '.profile']) { + writeFileSync(resolve(workspace, filename), profile, 'utf8'); + } + } +} + +export async function beforeEach({ test }) { + resetEvaluationWorkspace(test?.vars?.condition); + return { test }; +} diff --git a/benchmarks/codex-mcp/scripts/retrieval_trace.py b/benchmarks/codex-mcp/scripts/retrieval_trace.py new file mode 100644 index 00000000..ff1843ef --- /dev/null +++ b/benchmarks/codex-mcp/scripts/retrieval_trace.py @@ -0,0 +1,73 @@ +from __future__ import annotations + +import json +import sys +from collections.abc import Mapping +from typing import Any + +from vidxp.benchmarks.agent_ablation_score import _load_durable_job + + +def _retrieval_payload(job: Mapping[str, Any]) -> Mapping[str, Any]: + wrapper = job.get("result") + payload = wrapper.get("result") if isinstance(wrapper, Mapping) else None + if not isinstance(payload, Mapping): + raise ValueError("job has no typed retrieval result") + return payload + + +def _surface_candidates(payload: Mapping[str, Any]) -> list[dict[str, Any]]: + delivery = payload.get("evidence_delivery") + if not isinstance(delivery, Mapping): + return [] + board = delivery.get("board") + candidates = board.get("tiles") if isinstance(board, Mapping) else None + if not isinstance(candidates, list): + candidates = delivery.get("items") + if not isinstance(candidates, list): + return [] + + surfaced: list[dict[str, Any]] = [] + for item in candidates: + if not isinstance(item, Mapping): + continue + source_range = item.get("range") + if isinstance(source_range, Mapping): + start = source_range.get("source_start_seconds") + end = source_range.get("source_end_seconds") + else: + start = item.get("start") + end = item.get("end") + surfaced.append( + { + "evidence_id": item.get("evidence_id"), + "rank": item.get("rank"), + "start": start, + "end": end, + "modalities": item.get("modalities", []), + "state": item.get("state"), + } + ) + return surfaced + + +def main() -> int: + job_ids = tuple(dict.fromkeys(sys.argv[1:])) + if not job_ids: + raise SystemExit("usage: retrieval_trace.py JOB_ID [JOB_ID ...]") + + traces: dict[str, Mapping[str, Any]] = {} + for job_id in job_ids: + job = _load_durable_job(job_id) + payload = _retrieval_payload(job) + traces[job_id] = { + "query": payload.get("query", payload.get("question")), + "moments": payload.get("moments", []), + "surface_candidates": _surface_candidates(payload), + } + json.dump(traces, sys.stdout, separators=(",", ":")) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/run-eval.mjs b/benchmarks/codex-mcp/scripts/run-eval.mjs new file mode 100644 index 00000000..bfc2afcb --- /dev/null +++ b/benchmarks/codex-mcp/scripts/run-eval.mjs @@ -0,0 +1,90 @@ +import { spawnSync } from 'node:child_process'; +import { dirname, join, resolve } from 'node:path'; +import { fileURLToPath } from 'node:url'; + +import { loadLatestEvaluation, renderReport } from './report.mjs'; +import { prepareConditionState } from './condition-state.mjs'; + +const benchmarkRoot = resolve(dirname(fileURLToPath(import.meta.url)), '..'); +const repositoryRoot = resolve(benchmarkRoot, '..', '..'); +const mode = process.argv[2]; +if (!['smoke', 'pilot'].includes(mode)) { + throw new Error('Evaluation mode must be smoke or pilot.'); +} +const conditions = process.argv[3]; +const requestedConditions = conditions?.split(',').filter(Boolean) || []; +const localSlmOnly = requestedConditions.length > 0 && requestedConditions.every( + (condition) => condition === 'local-slm' || condition === 'local-slm-planner', +); +const evaluationEnvironment = { + ...process.env, + VIDXP_EVAL_MODE: mode, + ...(conditions ? { VIDXP_EVAL_CONDITIONS: conditions } : {}), +}; +if (!localSlmOnly) { + prepareConditionState({ repositoryRoot, environment: evaluationEnvironment }); +} + +const preflightCommands = localSlmOnly + ? [ + [ + evaluationEnvironment.PROMPTFOO_PYTHON, + [join(benchmarkRoot, 'scripts', 'local_slm_provider.py'), '--check'], + ], + [ + evaluationEnvironment.PROMPTFOO_PYTHON, + [join(benchmarkRoot, 'scripts', 'mcp_preflight.py')], + ], + ] + : [[process.execPath, [join(benchmarkRoot, 'scripts', 'preflight.mjs')]]]; +let preflightStatus = 0; +for (const [command, args] of preflightCommands) { + const preflight = spawnSync(command, args, { + cwd: repositoryRoot, + env: evaluationEnvironment, + stdio: 'inherit', + }); + if (preflight.status !== 0) { + if (preflight.error) { + console.error(`Preflight could not start ${command}: ${preflight.error.message}`); + } + preflightStatus = preflight.status ?? 1; + break; + } +} +if (preflightStatus !== 0) { + process.exitCode = preflightStatus; +} else { + let previousEvaluationId = null; + try { + previousEvaluationId = loadLatestEvaluation().id; + } catch { + // A first evaluation has no prior result. + } + const evaluation = spawnSync( + process.execPath, + [ + join(benchmarkRoot, 'node_modules', 'promptfoo', 'dist', 'src', 'entrypoint.js'), + 'eval', + '-c', + 'promptfooconfig.yaml', + '--no-cache', + '--no-share', + ], + { cwd: benchmarkRoot, env: evaluationEnvironment, stdio: 'inherit' }, + ); + let reportFailed = false; + try { + const latest = loadLatestEvaluation(); + if (latest.id === previousEvaluationId) { + throw new Error('Promptfoo did not save a new evaluation.'); + } + renderReport(latest); + } catch (error) { + console.error(`Could not report the completed evaluation: ${error.message}`); + reportFailed = true; + } + process.exitCode = evaluation.status === 0 && !reportFailed + ? 0 + : (evaluation.status || 1); +} diff --git a/benchmarks/codex-mcp/scripts/setup-lib.mjs b/benchmarks/codex-mcp/scripts/setup-lib.mjs index d4316561..8496bae6 100644 --- a/benchmarks/codex-mcp/scripts/setup-lib.mjs +++ b/benchmarks/codex-mcp/scripts/setup-lib.mjs @@ -1,7 +1,30 @@ +import { readFileSync } from 'node:fs'; import { homedir } from 'node:os'; import { posix, win32 } from 'node:path'; export const REQUIRED_NODE_VERSION = [22, 22, 0]; +const MACHINE_ID_PATTERN = /^[a-z0-9]+(?:-[a-z0-9]+)*$/; + +export function requireMachineId(value) { + if (typeof value !== 'string' || !MACHINE_ID_PATTERN.test(value)) { + throw new Error( + 'A repository machine ID such as mac-m2-01 is required; ' + + 'pass it to setup with --machine-id.', + ); + } + return value; +} + +export function savedMachineId(envPath) { + try { + const match = readFileSync(envPath, 'utf8').match( + /^VIDXP_EVAL_MACHINE_ID=(?:"([^"]+)"|'([^']+)'|([^\r\n]+))$/m, + ); + return match ? match[1] || match[2] || match[3] : null; + } catch { + return null; + } +} export function versionAtLeast(actual, required = REQUIRED_NODE_VERSION) { const parts = actual.split('.').map(Number); @@ -33,24 +56,67 @@ export function evaluationEnvironment({ benchmarkRoot, repositoryRoot, evaluationRoot, + indexSchemaVersion, environment = process.env, platform = process.platform, }) { + if (!Number.isInteger(indexSchemaVersion) || indexSchemaVersion < 1) { + throw new Error('A positive VidXP index schema version is required.'); + } const paths = platform === 'win32' ? win32 : posix; const executable = platform === 'win32' ? 'vidxp-mcp.exe' : 'vidxp-mcp'; + const pythonExecutable = platform === 'win32' ? 'python.exe' : 'python'; const scriptsDirectory = platform === 'win32' ? 'Scripts' : 'bin'; + const cleanUserPath = platform === 'win32' + ? [ + paths.join(environment.SystemRoot || 'C:\\Windows', 'System32'), + environment.SystemRoot || 'C:\\Windows', + ].join(';') + : '/usr/bin:/bin:/usr/sbin:/sbin'; + const machineId = requireMachineId(environment.VIDXP_EVAL_MACHINE_ID); return { + VIDXP_EVAL_MACHINE_ID: machineId, + VIDXP_EVAL_PROJECT_ROOT: repositoryRoot, VIDXP_EVAL_CODEX_HOME: paths.join(evaluationRoot, 'codex-home'), + VIDXP_EVAL_VIDXP_ON_CODEX_HOME: paths.join(evaluationRoot, 'codex-home', 'vidxp-on'), + VIDXP_EVAL_VIDXP_OFF_CODEX_HOME: paths.join(evaluationRoot, 'codex-home', 'vidxp-off'), + VIDXP_EVAL_CLEAN_USER_CODEX_HOME: paths.join( + evaluationRoot, + 'codex-home', + 'clean-user', + ), VIDXP_EVAL_WORKSPACE: paths.join(evaluationRoot, 'workspace'), + VIDXP_EVAL_VIDXP_ON_WORKSPACE: paths.join(evaluationRoot, 'workspace', 'vidxp-on'), + VIDXP_EVAL_VIDXP_OFF_WORKSPACE: paths.join(evaluationRoot, 'workspace', 'vidxp-off'), + VIDXP_EVAL_CLEAN_USER_WORKSPACE: paths.join( + evaluationRoot, + 'workspace', + 'clean-user', + ), + VIDXP_EVAL_CLEAN_USER_PATH: cleanUserPath, + VIDXP_EVAL_UV_CACHE_DIR: paths.join(evaluationRoot, 'uv-cache'), VIDXP_EVAL_DATA_DIR: paths.join(evaluationRoot, 'vidxp-data'), - VIDXP_EVAL_INDEX_DIR: paths.join(evaluationRoot, 'vidxp-index'), + VIDXP_EVAL_INDEX_DIR: paths.join( + evaluationRoot, + `vidxp-index-schema-${indexSchemaVersion}`, + ), VIDXP_MCP_COMMAND: paths.join(repositoryRoot, '.venv', scriptsDirectory, executable), + PROMPTFOO_PYTHON: paths.join( + repositoryRoot, + '.venv', + scriptsDirectory, + pythonExecutable, + ), VIDXP_EVAL_REPOSITORY: environment.VIDXP_EVAL_REPOSITORY || 'default', VIDXP_EVAL_DEVICE: environment.VIDXP_EVAL_DEVICE || 'cpu', VIDXP_EVAL_MODEL: environment.VIDXP_EVAL_MODEL || 'gpt-5.6-sol', VIDXP_EVAL_REASONING: environment.VIDXP_EVAL_REASONING || 'medium', VIDXP_EVAL_ARTIFACT_DIR: paths.join(evaluationRoot, 'longvale-artifacts'), VIDXP_EVAL_ENV_FILE: paths.join(benchmarkRoot, '.env'), + VIDXP_MODEL_CACHE: paths.resolve( + environment.VIDXP_MODEL_CACHE + || paths.join(evaluationRoot, 'vidxp-data', 'models'), + ), }; } diff --git a/benchmarks/codex-mcp/scripts/setup.mjs b/benchmarks/codex-mcp/scripts/setup.mjs index 43eab894..e138ea8a 100644 --- a/benchmarks/codex-mcp/scripts/setup.mjs +++ b/benchmarks/codex-mcp/scripts/setup.mjs @@ -1,11 +1,14 @@ import { createHash } from 'node:crypto'; import { spawnSync } from 'node:child_process'; +import { homedir } from 'node:os'; import { copyFileSync, createReadStream, existsSync, + linkSync, mkdirSync, readFileSync, + rmSync, writeFileSync, } from 'node:fs'; import { dirname, join, resolve } from 'node:path'; @@ -17,9 +20,12 @@ import { evaluationEnvironment, indexContainsPilot, libsqlBindingName, + requireMachineId, + savedMachineId, serializeEnvironment, versionAtLeast, } from './setup-lib.mjs'; +import { prepareConditionState } from './condition-state.mjs'; const benchmarkRoot = resolve(dirname(fileURLToPath(import.meta.url)), '..'); const repositoryRoot = resolve(benchmarkRoot, '..', '..'); @@ -29,11 +35,26 @@ const archiveHash = 'c83d62557f102c6d41ea95c2c3b3581657481c8646cc70b1e12a85ead27 const archiveRelativePath = join('raw_videos_test', 'LongVALE_test_1171_part_9.zip'); const annotationFilename = 'longvale-annotations-eval.json'; const modalities = ['scene', 'action', 'sound', 'speech']; - function executableName(command) { return process.platform === 'win32' && command === 'npm' ? 'npm.cmd' : command; } +function installedDesktopModelCache() { + const candidates = []; + if (process.platform === 'darwin') { + candidates.push(join(homedir(), 'Library', 'Application Support', 'VidXP', 'models')); + } else if (process.platform === 'win32' && process.env.LOCALAPPDATA) { + candidates.push(join(process.env.LOCALAPPDATA, 'VidXP', 'models')); + } else if (process.platform === 'linux') { + candidates.push(join( + process.env.XDG_DATA_HOME || join(homedir(), '.local', 'share'), + 'VidXP', + 'models', + )); + } + return candidates.find((candidate) => existsSync(candidate)); +} + function formatCommand(command, args) { return [command, ...args] .map((part) => (/\s/.test(part) ? JSON.stringify(part) : part)) @@ -92,22 +113,60 @@ async function main() { } run('uv', ['--version'], { capture: true }); - run('codex', ['--version'], { capture: true }); + + const argumentsList = process.argv.slice(2); + let requestedMachineId = null; + if (argumentsList.length > 0) { + if (argumentsList.length !== 2 || argumentsList[0] !== '--machine-id') { + throw new Error('Usage: setup --machine-id '); + } + requestedMachineId = argumentsList[1]; + } const evaluationRoot = defaultEvaluationRoot(process.env); + const uvCacheDirectory = join(evaluationRoot, 'uv-cache'); + mkdirSync(uvCacheDirectory, { recursive: true }); + const uvEnvironment = { ...process.env, UV_CACHE_DIR: uvCacheDirectory }; + const desktopModelCache = installedDesktopModelCache(); + const machineId = requireMachineId( + requestedMachineId + || process.env.VIDXP_EVAL_MACHINE_ID + || savedMachineId(join(benchmarkRoot, '.env')), + ); + const setupSourceEnvironment = { + ...process.env, + VIDXP_EVAL_MACHINE_ID: machineId, + ...(process.env.VIDXP_MODEL_CACHE || !desktopModelCache + ? {} + : { VIDXP_MODEL_CACHE: desktopModelCache }), + }; + run( + 'uv', + [ + 'sync', '--frozen', '--extra', 'local-worker', '--extra', 'mcp', '--extra', 'server', + '--extra', 'benchmarks', '--extra', 'test', + ], + { env: uvEnvironment }, + ); + const indexSchemaVersion = Number(run( + 'uv', + [ + 'run', '--no-sync', 'python', '-c', + 'from vidxp.core.contracts import INDEX_SCHEMA_VERSION; print(INDEX_SCHEMA_VERSION)', + ], + { capture: true, env: uvEnvironment }, + ).trim()); const setupEnvironment = evaluationEnvironment({ benchmarkRoot, repositoryRoot, evaluationRoot, + indexSchemaVersion, + environment: setupSourceEnvironment, }); const commandEnvironment = { ...process.env, ...setupEnvironment }; const tasks = JSON.parse(readFileSync(manifestPath, 'utf8')); const videoIds = [...new Set(tasks.map((task) => task.video_id))]; - run( - 'uv', - ['sync', '--frozen', '--extra', 'local-worker', '--extra', 'mcp', '--extra', 'benchmarks'], - ); run( 'uv', ['run', '--no-sync', 'vidxp', 'init', '--yes'], @@ -125,14 +184,25 @@ async function main() { if (!bindingVersion) { throw new Error(`The Promptfoo lock does not declare ${bindingName}.`); } + const codexManifest = JSON.parse(readFileSync( + join(benchmarkRoot, 'node_modules', '@openai', 'codex', 'package.json'), + 'utf8', + )); + const codexBindingName = `@openai/codex-${process.platform}-${process.arch}`; + const codexBindingVersion = codexManifest.optionalDependencies?.[codexBindingName]; + if (!codexBindingVersion) { + throw new Error(`The pinned Codex package does not support ${process.platform}-${process.arch}.`); + } run( 'npm', [ - 'install', '--no-save', '--package-lock=false', '--omit=optional', + 'install', '--no-save', '--package-lock=false', `${bindingName}@${bindingVersion}`, + `${codexBindingName}@${codexBindingVersion}`, ], { cwd: benchmarkRoot }, ); + run('codex', ['--version'], { capture: true }); run( process.execPath, [ @@ -144,17 +214,45 @@ async function main() { for (const directory of [ setupEnvironment.VIDXP_EVAL_CODEX_HOME, + setupEnvironment.VIDXP_EVAL_VIDXP_ON_CODEX_HOME, + setupEnvironment.VIDXP_EVAL_VIDXP_OFF_CODEX_HOME, + setupEnvironment.VIDXP_EVAL_CLEAN_USER_CODEX_HOME, + setupEnvironment.VIDXP_EVAL_UV_CACHE_DIR, setupEnvironment.VIDXP_EVAL_WORKSPACE, join(setupEnvironment.VIDXP_EVAL_WORKSPACE, 'media'), + setupEnvironment.VIDXP_EVAL_VIDXP_ON_WORKSPACE, + setupEnvironment.VIDXP_EVAL_VIDXP_OFF_WORKSPACE, + join(setupEnvironment.VIDXP_EVAL_VIDXP_OFF_WORKSPACE, 'media'), + setupEnvironment.VIDXP_EVAL_CLEAN_USER_WORKSPACE, + join(setupEnvironment.VIDXP_EVAL_CLEAN_USER_WORKSPACE, 'media'), + join(setupEnvironment.VIDXP_EVAL_CLEAN_USER_WORKSPACE, 'bin'), + join(setupEnvironment.VIDXP_EVAL_CLEAN_USER_WORKSPACE, 'tmp'), setupEnvironment.VIDXP_EVAL_DATA_DIR, setupEnvironment.VIDXP_EVAL_INDEX_DIR, setupEnvironment.VIDXP_EVAL_ARTIFACT_DIR, ]) { mkdirSync(directory, { recursive: true }); } + rmSync( + join(setupEnvironment.VIDXP_EVAL_VIDXP_ON_WORKSPACE, 'media'), + { recursive: true, force: true }, + ); + prepareConditionState({ repositoryRoot, environment: commandEnvironment }); if (!existsSync(setupEnvironment.VIDXP_MCP_COMMAND)) { throw new Error(`VidXP MCP executable was not created at ${setupEnvironment.VIDXP_MCP_COMMAND}.`); } + if (process.platform !== 'win32') { + const cleanPathProfile = `export PATH=${JSON.stringify( + setupEnvironment.VIDXP_EVAL_CLEAN_USER_PATH, + )}\n`; + for (const profile of ['.zshenv', '.zprofile', '.profile']) { + writeFileSync( + join(setupEnvironment.VIDXP_EVAL_CLEAN_USER_WORKSPACE, profile), + cleanPathProfile, + 'utf8', + ); + } + } writeFileSync( setupEnvironment.VIDXP_EVAL_ENV_FILE, serializeEnvironment(setupEnvironment), @@ -171,6 +269,13 @@ async function main() { if (!existsSync(authPath)) { throw new Error('Codex login completed without creating auth.json in the isolated profile.'); } + for (const conditionHome of [ + setupEnvironment.VIDXP_EVAL_VIDXP_ON_CODEX_HOME, + setupEnvironment.VIDXP_EVAL_VIDXP_OFF_CODEX_HOME, + setupEnvironment.VIDXP_EVAL_CLEAN_USER_CODEX_HOME, + ]) { + copyFileSync(authPath, join(conditionHome, 'auth.json')); + } process.stdout.write( '\nDownloading the pinned LongVALE pilot files. Use of the dataset is subject to its published terms.\n', @@ -207,9 +312,30 @@ async function main() { if (!existsSync(source)) { throw new Error(`The LongVALE archive did not contain ${source}.`); } - copyFileSync(source, join(setupEnvironment.VIDXP_EVAL_WORKSPACE, 'media', `${videoId}.mp4`)); + const sharedMedia = join(setupEnvironment.VIDXP_EVAL_WORKSPACE, 'media', `${videoId}.mp4`); + copyFileSync(source, sharedMedia); + for (const conditionWorkspace of [ + setupEnvironment.VIDXP_EVAL_VIDXP_OFF_WORKSPACE, + setupEnvironment.VIDXP_EVAL_CLEAN_USER_WORKSPACE, + ]) { + const conditionMedia = join(conditionWorkspace, 'media', `${videoId}.mp4`); + if (!existsSync(conditionMedia)) { + linkSync(sharedMedia, conditionMedia); + } + } } + run( + 'uv', + [ + 'run', '--no-sync', 'vidxp', + '--data-dir', setupEnvironment.VIDXP_EVAL_DATA_DIR, + '--index-dir', setupEnvironment.VIDXP_EVAL_INDEX_DIR, + 'jobs', 'stop-worker', + ], + { env: commandEnvironment }, + ); + run( 'uv', [ @@ -221,8 +347,13 @@ async function main() { { env: commandEnvironment }, ); - if (!indexContainsPilot(readIndex(setupEnvironment), videoIds, modalities)) { + const currentIndex = readIndex(setupEnvironment); + if (!indexContainsPilot(currentIndex, videoIds, modalities)) { for (const videoId of videoIds) { + if (indexContainsPilot(currentIndex, [videoId], modalities)) { + process.stdout.write(`\n${videoId}.mp4 is already indexed; skipping.\n`); + continue; + } process.stdout.write(`\nIndexing ${videoId}.mp4\n`); const mediaPath = join(setupEnvironment.VIDXP_EVAL_WORKSPACE, 'media', `${videoId}.mp4`); const imported = JSON.parse(run( @@ -259,8 +390,9 @@ async function main() { process.stdout.write( '\nSetup complete. Run:\n' - + ' npm --prefix benchmarks/codex-mcp run eval:smoke\n' - + ' npm --prefix benchmarks/codex-mcp run eval:pilot\n', + + ' ./benchmarks/codex-mcp/run smoke\n' + + ' ./benchmarks/codex-mcp/run pilot\n' + + ' ./benchmarks/codex-mcp/run view\n', ); } diff --git a/benchmarks/codex-mcp/scripts/setup.test.mjs b/benchmarks/codex-mcp/scripts/setup.test.mjs index 0d0cf18e..8a8e85bf 100644 --- a/benchmarks/codex-mcp/scripts/setup.test.mjs +++ b/benchmarks/codex-mcp/scripts/setup.test.mjs @@ -1,4 +1,14 @@ import assert from 'node:assert/strict'; +import { + existsSync, + mkdirSync, + mkdtempSync, + readFileSync, + rmSync, + writeFileSync, +} from 'node:fs'; +import { tmpdir } from 'node:os'; +import { join } from 'node:path'; import { test } from 'node:test'; import { @@ -6,9 +16,16 @@ import { evaluationEnvironment, indexContainsPilot, libsqlBindingName, + requireMachineId, + savedMachineId, serializeEnvironment, versionAtLeast, } from './setup-lib.mjs'; +import { + executableInstallRoots, + permissionProfile, +} from './condition-state.mjs'; +import { resetEvaluationWorkspace } from './reset-workspace.mjs'; test('checks the required Node version numerically', () => { assert.equal(versionAtLeast('22.21.9'), false); @@ -62,14 +79,130 @@ test('builds and serializes the environment consumed by Promptfoo', () => { benchmarkRoot: 'C:/repo/benchmarks/codex-mcp', repositoryRoot: 'C:/repo', evaluationRoot: 'C:/eval', - environment: {}, + indexSchemaVersion: 8, + environment: { + VIDXP_EVAL_MACHINE_ID: 'win-test-01', + VIDXP_MODEL_CACHE: 'C:/shared-models', + }, platform: 'win32', }); const serialized = serializeEnvironment(environment); assert.match(serialized, /VIDXP_EVAL_WORKSPACE="C:\/eval\/workspace"/); + assert.match(serialized, /VIDXP_EVAL_PROJECT_ROOT="C:\/repo"/); + assert.match(serialized, /VIDXP_EVAL_MACHINE_ID="win-test-01"/); + assert.match(serialized, /VIDXP_EVAL_INDEX_DIR="C:\/eval\/vidxp-index-schema-8"/); + assert.match(serialized, /VIDXP_EVAL_VIDXP_ON_WORKSPACE="C:\/eval\/workspace\/vidxp-on"/); + assert.match(serialized, /VIDXP_EVAL_VIDXP_OFF_WORKSPACE="C:\/eval\/workspace\/vidxp-off"/); + assert.match( + serialized, + /VIDXP_EVAL_CLEAN_USER_WORKSPACE="C:\/eval\/workspace\/clean-user"/, + ); + assert.match(serialized, /VIDXP_EVAL_VIDXP_ON_CODEX_HOME="C:\/eval\/codex-home\/vidxp-on"/); + assert.match(serialized, /VIDXP_EVAL_VIDXP_OFF_CODEX_HOME="C:\/eval\/codex-home\/vidxp-off"/); + assert.match(serialized, /VIDXP_EVAL_CLEAN_USER_CODEX_HOME="C:\/eval\/codex-home\/clean-user"/); + assert.match( + serialized, + /VIDXP_EVAL_CLEAN_USER_PATH="C:\/Windows\/System32;C:\/Windows"/, + ); + assert.match(serialized, /VIDXP_EVAL_UV_CACHE_DIR="C:\/eval\/uv-cache"/); assert.match(serialized, /VIDXP_MCP_COMMAND="C:\/repo\/\.venv\/Scripts\/vidxp-mcp\.exe"/); + assert.match(serialized, /PROMPTFOO_PYTHON="C:\/repo\/\.venv\/Scripts\/python\.exe"/); assert.match(serialized, /VIDXP_EVAL_MODEL="gpt-5\.6-sol"/); + assert.match(serialized, /VIDXP_MODEL_CACHE="C:\/shared-models"/); assert.doesNotMatch(serialized, /VIDXP_EVAL_ENV_FILE/); assert.doesNotMatch(serialized, /VIDXP_EVAL_ARTIFACT_DIR/); }); + +test('always records the model cache used by the isolated runtime', () => { + const environment = evaluationEnvironment({ + benchmarkRoot: '/repo/benchmarks/codex-mcp', + repositoryRoot: '/repo', + evaluationRoot: '/eval', + indexSchemaVersion: 8, + environment: { VIDXP_EVAL_MACHINE_ID: 'linux-test-01' }, + platform: 'linux', + }); + + assert.equal(environment.VIDXP_MODEL_CACHE, '/eval/vidxp-data/models'); +}); + +test('builds a root-denied Codex profile with explicit condition capabilities', () => { + assert.deepEqual( + executableInstallRoots(['/opt/homebrew/bin/ffmpeg', '/opt/homebrew/bin/ffprobe']), + ['/opt/homebrew'], + ); + const directLocal = permissionProfile({ + networkEnabled: false, + readableRoots: ['/opt/homebrew'], + }); + const cleanUser = permissionProfile({ networkEnabled: true }); + + assert.match(directLocal, /":root" = "deny"/); + assert.match(directLocal, /":minimal" = "read"/); + assert.match(directLocal, /"\/opt\/homebrew" = "read"/); + assert.match(directLocal, /enabled = false/); + assert.doesNotMatch(cleanUser, /opt\/homebrew/); + assert.match(cleanUser, /enabled = true/); +}); + +test('requires and reloads a stable repository machine ID', () => { + assert.equal(requireMachineId('mac-m2-01'), 'mac-m2-01'); + assert.throws(() => requireMachineId('MacBook Pro'), /machine ID/); + + const root = mkdtempSync(join(tmpdir(), 'vidxp-eval-machine-')); + const envPath = join(root, '.env'); + writeFileSync(envPath, 'VIDXP_EVAL_MACHINE_ID="mac-m2-01"\n'); + assert.equal(savedMachineId(envPath), 'mac-m2-01'); + rmSync(root, { recursive: true, force: true }); +}); + +test('resets clean-user state before every condition run', () => { + const root = mkdtempSync(join(tmpdir(), 'vidxp-eval-reset-')); + const workspaceRoot = join(root, 'workspace'); + const cleanWorkspace = join(workspaceRoot, 'clean-user'); + mkdirSync(join(cleanWorkspace, 'media'), { recursive: true }); + mkdirSync(join(cleanWorkspace, '.cache'), { recursive: true }); + writeFileSync(join(cleanWorkspace, '.cache', 'installed-tool'), 'stale'); + + resetEvaluationWorkspace('clean-user', { + VIDXP_EVAL_WORKSPACE: workspaceRoot, + VIDXP_EVAL_CLEAN_USER_WORKSPACE: cleanWorkspace, + VIDXP_EVAL_CLEAN_USER_PATH: '/usr/bin:/bin', + }); + + assert.equal(existsSync(join(cleanWorkspace, '.cache')), false); + assert.equal(existsSync(join(cleanWorkspace, 'media')), true); + assert.equal(existsSync(join(cleanWorkspace, 'tmp')), true); + assert.equal(existsSync(join(cleanWorkspace, 'bin')), true); + assert.equal( + readFileSync(join(cleanWorkspace, '.zshenv'), 'utf8'), + 'export PATH="/usr/bin:/bin"\n', + ); + rmSync(root, { recursive: true, force: true }); +}); + +test('does not reset a workspace for either tool-only local agent', () => { + assert.doesNotThrow(() => resetEvaluationWorkspace('local-slm', {})); + assert.doesNotThrow(() => resetEvaluationWorkspace('local-slm-planner', {})); +}); + +test('removes source media while retaining the VidXP-on skill', () => { + const root = mkdtempSync(join(tmpdir(), 'vidxp-eval-reset-')); + const workspaceRoot = join(root, 'workspace'); + const onWorkspace = join(workspaceRoot, 'vidxp-on'); + mkdirSync(join(onWorkspace, 'media'), { recursive: true }); + mkdirSync(join(onWorkspace, '.agents'), { recursive: true }); + writeFileSync(join(onWorkspace, 'media', 'video.mp4'), 'source'); + writeFileSync(join(onWorkspace, '.agents', 'skill'), 'installed'); + + resetEvaluationWorkspace('vidxp-on', { + VIDXP_EVAL_WORKSPACE: workspaceRoot, + VIDXP_EVAL_VIDXP_ON_WORKSPACE: onWorkspace, + }); + + assert.equal(existsSync(join(onWorkspace, 'media')), false); + assert.equal(existsSync(join(onWorkspace, '.agents', 'skill')), true); + assert.equal(existsSync(join(onWorkspace, 'tmp')), true); + rmSync(root, { recursive: true, force: true }); +}); diff --git a/benchmarks/codex-mcp/scripts/shot_proposal_control.py b/benchmarks/codex-mcp/scripts/shot_proposal_control.py new file mode 100644 index 00000000..62f5009f --- /dev/null +++ b/benchmarks/codex-mcp/scripts/shot_proposal_control.py @@ -0,0 +1,637 @@ +from __future__ import annotations + +import argparse +import json +import os +import shlex +import sys +import time +from pathlib import Path +from typing import Any + +# PySceneDetect eagerly imports every installed backend. Keep its optional PyAV +# backend unloaded so macOS does not load PyAV and OpenCV FFmpeg libraries into +# this process together; this control explicitly uses the OpenCV backend. +sys.modules["av"] = None + +from scenedetect import ContentDetector, detect # noqa: E402 + +from vidxp.benchmarks.agent_ablation_score import interval_iou # noqa: E402 +from vidxp.benchmarks.shot_proposals import ( # noqa: E402 + DIWAN_CONTENT_THRESHOLD, + DIWAN_PAPER_URL, + TemporalShot, + rank_shots_from_scene_records, + rank_shots_with_rrf_evidence, +) +from vidxp.search_fusion import RRF_RANK_CONSTANT # noqa: E402 + + +BENCHMARK_ROOT = Path(__file__).resolve().parent.parent +TASKS_PATH = BENCHMARK_ROOT / "tasks" / "longvale-part9-pilot.json" +BOUNDARY_TOLERANCE_SECONDS = 0.05 + + +def _load_environment() -> None: + path = BENCHMARK_ROOT / ".env" + if not path.is_file(): + raise RuntimeError("run benchmark setup before comparing shot proposals") + for raw_line in path.read_text(encoding="utf-8").splitlines(): + line = raw_line.strip() + if not line or line.startswith("#") or "=" not in line: + continue + name, raw_value = line.split("=", 1) + values = shlex.split(raw_value, posix=True) + if len(values) != 1: + raise RuntimeError(f"invalid value for {name} in benchmark .env") + os.environ.setdefault(name, values[0]) + + +def _required_environment(name: str) -> str: + value = os.environ.get(name) + if not value: + raise RuntimeError(f"{name} is missing from benchmark .env") + return value + + +def _tasks() -> list[dict[str, Any]]: + return json.loads(TASKS_PATH.read_text(encoding="utf-8")) + + +def _task(task_id: str) -> dict[str, Any]: + tasks = _tasks() + matches = [task for task in tasks if task.get("id") == task_id] + if len(matches) != 1: + raise ValueError(f"unknown task id: {task_id}") + return matches[0] + + +def _probe_path(task_id: str) -> Path: + root = Path(_required_environment("VIDXP_EVAL_DATA_DIR")).parent + return root / "localization" / f"{task_id}.probe.json" + + +def _detect_shots(source: Path) -> tuple[tuple[TemporalShot, ...], float]: + started = time.perf_counter() + detected = detect( + str(source), + ContentDetector(threshold=DIWAN_CONTENT_THRESHOLD), + show_progress=False, + ) + detection_seconds = time.perf_counter() - started + return ( + tuple( + TemporalShot(start=start.seconds, end=end.seconds) + for start, end in detected + ), + detection_seconds, + ) + + +def compare_shot_proposals( + task_id: str, + *, + detected_shots: tuple[TemporalShot, ...] | None = None, + detection_seconds: float | None = None, + detection_reused: bool = False, +) -> dict: + _load_environment() + task = _task(task_id) + base_path = _probe_path(task_id) + if not base_path.is_file(): + raise RuntimeError(f"run './benchmarks/codex-mcp/run probe {task_id}' first") + probe = json.loads(base_path.read_text(encoding="utf-8")) + source = Path(_required_environment("VIDXP_EVAL_WORKSPACE")) / task["media_relpath"] + if not source.is_file(): + raise RuntimeError(f"prepared benchmark media is missing: {source}") + + if detected_shots is None: + shots, measured_detection_seconds = _detect_shots(source) + detection_seconds = measured_detection_seconds + else: + shots = detected_shots + if detection_seconds is None: + raise ValueError("detection_seconds is required with detected_shots") + if not shots: + raise RuntimeError("PySceneDetect produced no proposals") + scene_result = probe["modalities"].get("scene") + ranked = ( + rank_shots_from_scene_records(shots, scene_result["records"]) + if scene_result + else () + ) + if scene_result and not ranked: + raise RuntimeError( + "PySceneDetect produced no proposal containing a scene sample" + ) + candidate_top_k = int( + probe["current_control"]["candidate_top_k_per_modality"] + ) + fused = rank_shots_with_rrf_evidence( + shots, + { + modality: result["records"] + for modality, result in probe["modalities"].items() + }, + scene_ranking=ranked, + candidate_top_k=candidate_top_k, + rank_constant=RRF_RANK_CONSTANT, + ) + if not fused: + raise RuntimeError("no proposal overlaps the saved top-k evidence") + + expected_start = float(task["expected_start"]) + expected_end = float(task["expected_end"]) + top = ranked[0] if ranked else None + top_fused = fused[0] + oracle = max( + shots, + key=lambda shot: interval_iou( + shot.start, + shot.end, + expected_start, + expected_end, + ), + ) + + def metrics(shot) -> dict: + return { + "start_seconds": shot.start, + "end_seconds": shot.end, + "temporal_iou": interval_iou( + shot.start, + shot.end, + expected_start, + expected_end, + ), + "start_error_seconds": shot.start - expected_start, + "end_error_seconds": shot.end - expected_end, + } + + output = base_path.with_name(base_path.name.replace(".probe.json", ".shots.json")) + oracle_metrics = metrics(oracle) + internal_boundaries = sorted( + shot.end + for shot in shots[:-1] + if expected_start + BOUNDARY_TOLERANCE_SECONDS + < shot.end + < expected_end - BOUNDARY_TOLERANCE_SECONDS + ) + + def fused_payload(shot) -> dict: + return { + **metrics(shot), + "score": shot.score, + "scene_rank": shot.scene_rank, + "best_ranks": dict(shot.best_ranks), + "evidence": [ + { + "modality": item.modality, + "rank": item.rank, + "source_id": item.source_id, + "proposal_overlap_count": item.proposal_overlap_count, + } + for item in shot.evidence + ], + } + + adaptations = [ + ( + "rank fixed shot candidates with VidXP RRF using the best " + "overlapping top-k evidence rank per non-scene modality" + ) + ] + if scene_result: + adaptations[:0] = [ + "reuse VidXP one-fps SigLIP2 records instead of CLIP-ViT-B/32", + "reuse globally sampled frames instead of sampling within each shot", + "rank each shot by its maximum contained scene ordering score", + ] + + payload = { + "schema_version": 3, + "task_id": task_id, + "declared_modalities": task["modalities"], + "ground_truth": { + "start_seconds": expected_start, + "end_seconds": expected_end, + "detected_boundaries_inside": internal_boundaries, + "spans_multiple_detected_shots": bool(internal_boundaries), + "boundary_tolerance_seconds": BOUNDARY_TOLERANCE_SECONDS, + }, + "method": { + "paper": DIWAN_PAPER_URL, + "component": "ShotDetect proposals without SimpleWatershed", + "published_content_threshold": DIWAN_CONTENT_THRESHOLD, + "pyscenedetect_version": "0.7", + "rrf_non_scene_evidence_top_k": candidate_top_k, + "rrf_rank_constant": RRF_RANK_CONSTANT, + "rrf_boundary_rule": "keep the selected shot interval unchanged", + "scene_matcher_applied": bool(scene_result), + "adaptations": adaptations, + "excluded": [ + "SimpleWatershed and its QVHighlights-tuned similarity threshold", + "video captioning matcher", + ], + }, + "control": probe["current_control"], + "top_retrieved": metrics(top) if top else None, + "top_rrf_proposal": fused_payload(top_fused), + "best_proposal_oracle": { + **oracle_metrics, + "scene_retrieval_rank": next( + ( + shot.rank + for shot in ranked + if shot.start == oracle.start and shot.end == oracle.end + ), + None, + ), + }, + "recall": { + f"tiou_{threshold}": oracle_metrics["temporal_iou"] >= threshold + for threshold in (0.3, 0.5, 0.7) + }, + "resource_use": { + "detection_seconds": detection_seconds, + "detection_reused": detection_reused, + "detected_proposals": len(shots), + "scene_scored_proposals": len(ranked), + "rrf_scored_proposals": len(fused), + "scene_records_reused": len(scene_result["records"]) if scene_result else 0, + "model_calls": 0, + "stored_bytes": 0, + }, + "probe_resource_use": { + "elapsed_seconds": probe["elapsed_seconds"], + "text_embedding_calls": sum( + result["model_calls"]["text_embedding"] + for result in probe["modalities"].values() + ), + }, + "proposals": [ + { + "rank": shot.rank, + "start_seconds": shot.start, + "end_seconds": shot.end, + "ordering_score": shot.score, + "source_ids": shot.source_ids, + } + for shot in ranked + ], + "rrf_proposals": [ + { + "rank": shot.rank, + "scene_rank": shot.scene_rank, + "start_seconds": shot.start, + "end_seconds": shot.end, + "score": shot.score, + "best_ranks": dict(shot.best_ranks), + "source_ids": shot.source_ids, + "evidence": [ + { + "modality": item.modality, + "rank": item.rank, + "source_id": item.source_id, + "proposal_overlap_count": item.proposal_overlap_count, + } + for item in shot.evidence + ], + } + for shot in fused + ], + } + output.write_text( + json.dumps(payload, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + return { + "output": str(output), + "task_id": task_id, + "declared_modalities": task["modalities"], + "ground_truth": payload["ground_truth"], + "control": probe["current_control"]["top_moment_metrics"], + "top_retrieved": payload["top_retrieved"], + "top_rrf_proposal": payload["top_rrf_proposal"], + "best_proposal_oracle": payload["best_proposal_oracle"], + "recall": payload["recall"], + "resource_use": payload["resource_use"], + "probe_resource_use": payload["probe_resource_use"], + } + + +def _method_summary(results: list[dict], key: str) -> dict: + values = [result[key] for result in results if result[key] is not None] + return { + "tasks": len(values), + "mean_temporal_iou": sum(item["temporal_iou"] for item in values) + / len(values), + "threshold_rates": { + f"tiou_{threshold}": sum( + item["temporal_iou"] >= threshold for item in values + ) + / len(values) + for threshold in (0.3, 0.5, 0.7) + }, + "mean_absolute_start_error_seconds": sum( + abs(item["start_error_seconds"]) for item in values + ) + / len(values), + "mean_absolute_end_error_seconds": sum( + abs(item["end_error_seconds"]) for item in values + ) + / len(values), + } + + +def compare_held_out() -> dict: + _load_environment() + tasks = _tasks()[2:] + missing = [task["id"] for task in tasks if not _probe_path(task["id"]).is_file()] + if missing: + raise RuntimeError( + "missing held-out probes; run './benchmarks/codex-mcp/run probe TASK_ID' " + f"for: {', '.join(missing)}" + ) + + detected: dict[str, tuple[tuple[TemporalShot, ...], float]] = {} + results = [] + for task in tasks: + source = ( + Path(_required_environment("VIDXP_EVAL_WORKSPACE")) + / task["media_relpath"] + ) + cache_key = str(source) + reused = cache_key in detected + if not reused: + detected[cache_key] = _detect_shots(source) + shots, seconds = detected[cache_key] + results.append( + compare_shot_proposals( + task["id"], + detected_shots=shots, + detection_seconds=seconds, + detection_reused=reused, + ) + ) + + scene_comparisons = [] + for result in results: + scene = result["top_retrieved"] + if scene is None: + continue + fused = result["top_rrf_proposal"] + same = (scene["start_seconds"], scene["end_seconds"]) == ( + fused["start_seconds"], + fused["end_seconds"], + ) + delta = fused["temporal_iou"] - scene["temporal_iou"] + if same: + outcome = "unchanged" + elif delta > 1e-12: + outcome = "changed_helped" + elif delta < -1e-12: + outcome = "changed_hurt" + else: + outcome = "changed_same_iou" + scene_comparisons.append( + { + "task_id": result["task_id"], + "outcome": outcome, + "iou_delta": delta, + } + ) + + ambiguous = [ + { + "task_id": result["task_id"], + "evidence": [ + item + for item in result["top_rrf_proposal"]["evidence"] + if item["proposal_overlap_count"] > 1 + ], + } + for result in results + ] + ambiguous = [item for item in ambiguous if item["evidence"]] + failure_split = { + "boundary_limited": [ + result["task_id"] + for result in results + if result["best_proposal_oracle"]["temporal_iou"] < 0.5 + ], + "ranking_limited": [ + result["task_id"] + for result in results + if result["best_proposal_oracle"]["temporal_iou"] >= 0.5 + and result["top_rrf_proposal"]["temporal_iou"] < 0.5 + ], + } + scene_results = [result for result in results if result["top_retrieved"]] + no_scene_results = [result for result in results if not result["top_retrieved"]] + aggregate = { + "schema_version": 1, + "scope": "held-out tasks 3-10 from the Codex MCP pilot manifest", + "tasks": len(results), + "scene_comparable_tasks": len(scene_comparisons), + "methods": { + "current_connected_union": _method_summary(results, "control"), + "scene_ranked_shot": _method_summary(results, "top_retrieved"), + "proposal_preserving_rrf": _method_summary( + results, + "top_rrf_proposal", + ), + "current_union_scene_comparable": _method_summary( + scene_results, + "control", + ), + "rrf_scene_comparable": _method_summary( + scene_results, + "top_rrf_proposal", + ), + "current_union_without_scene": _method_summary( + no_scene_results, + "control", + ), + "rrf_without_scene": _method_summary( + no_scene_results, + "top_rrf_proposal", + ), + "best_single_shot_oracle": _method_summary( + results, + "best_proposal_oracle", + ), + }, + "scene_vs_rrf": { + "unchanged": sum( + item["outcome"] == "unchanged" for item in scene_comparisons + ), + "changed_helped": sum( + item["outcome"] == "changed_helped" for item in scene_comparisons + ), + "changed_hurt": sum( + item["outcome"] == "changed_hurt" for item in scene_comparisons + ), + "changed_same_iou": sum( + item["outcome"] == "changed_same_iou" + for item in scene_comparisons + ), + "tasks": scene_comparisons, + }, + "top_rrf_ambiguous_evidence": { + "tasks": len(ambiguous), + "details": ambiguous, + }, + "failure_split_at_tiou_0_5": failure_split, + "ground_truth_spans_multiple_detected_shots": [ + result["task_id"] + for result in results + if result["ground_truth"]["spans_multiple_detected_shots"] + ], + "unique_videos": len(detected), + "resource_use": { + "shot_detection_seconds": sum( + seconds for _, seconds in detected.values() + ), + "probe_elapsed_seconds": sum( + result["probe_resource_use"]["elapsed_seconds"] + for result in results + ), + "local_text_embedding_calls": sum( + result["probe_resource_use"]["text_embedding_calls"] + for result in results + ), + "shot_model_calls": 0, + "codex_calls": 0, + "new_index_bytes": 0, + "peak_memory_bytes": None, + }, + "results": results, + } + output = _probe_path("held-out").with_name("shots-held-out.json") + output.write_text( + json.dumps(aggregate, ensure_ascii=False, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + return { + "output": str(output), + "scope": aggregate["scope"], + "tasks": aggregate["tasks"], + "scene_comparable_tasks": aggregate["scene_comparable_tasks"], + "methods": aggregate["methods"], + "scene_vs_rrf": aggregate["scene_vs_rrf"], + "failure_split_at_tiou_0_5": failure_split, + "ambiguous_evidence_tasks": [ + item["task_id"] for item in ambiguous + ], + "ground_truth_spans_multiple_detected_shots": aggregate[ + "ground_truth_spans_multiple_detected_shots" + ], + "resource_use": aggregate["resource_use"], + "per_task": [ + { + "task_id": result["task_id"], + "current_iou": result["control"]["temporal_iou"], + "scene_iou": ( + result["top_retrieved"]["temporal_iou"] + if result["top_retrieved"] + else None + ), + "rrf_iou": result["top_rrf_proposal"]["temporal_iou"], + "best_shot_iou": result["best_proposal_oracle"]["temporal_iou"], + } + for result in results + ], + } + + +def _print_held_out(result: dict) -> None: + methods = result["methods"] + rows = ( + ("Current union (8)", methods["current_connected_union"]), + ("Best single shot (8)", methods["best_single_shot_oracle"]), + ("Scene-ranked shot (6)", methods["scene_ranked_shot"]), + ("RRF, same scene tasks (6)", methods["rrf_scene_comparable"]), + ("RRF, no-scene tasks (2)", methods["rrf_without_scene"]), + ) + print("Held-out shot comparison") + print("Method mean IoU >=.3 >=.5 >=.7") + for label, metrics in rows: + recall = metrics["threshold_rates"] + print( + f"{label:<27} {metrics['mean_temporal_iou']:>7.4f} " + f"{recall['tiou_0.3']:>7.3f} {recall['tiou_0.5']:>7.3f} " + f"{recall['tiou_0.7']:>7.3f}" + ) + + comparison = result["scene_vs_rrf"] + print( + "\nScene vs RRF: " + f"{comparison['unchanged']} unchanged, " + f"{comparison['changed_helped']} helped, " + f"{comparison['changed_hurt']} hurt, " + f"{comparison['changed_same_iou']} changed with equal IoU." + ) + print("\nTask current scene RRF best shot") + for task in result["per_task"]: + label = task["task_id"].removeprefix("longvale-part9-") + scene = ( + "n/a" + if task["scene_iou"] is None + else f"{task['scene_iou']:.4f}" + ) + print( + f"{label:<29} {task['current_iou']:>7.4f} {scene:>7} " + f"{task['rrf_iou']:>7.4f} {task['best_shot_iou']:>11.4f}" + ) + + failures = result["failure_split_at_tiou_0_5"] + def short(values: list[str]) -> str: + return ", ".join( + value.removeprefix("longvale-part9-") for value in values + ) + print(f"\nBoundary-limited at tIoU .5: {short(failures['boundary_limited'])}") + print(f"Ranking-limited at tIoU .5: {short(failures['ranking_limited'])}") + print( + "Ambiguous evidence: " + f"{len(result['ambiguous_evidence_tasks'])}/{result['tasks']} tasks" + ) + print( + "References crossing detected cuts: " + f"{len(result['ground_truth_spans_multiple_detected_shots'])}" + ) + resources = result["resource_use"] + print( + "Resource use: " + f"{resources['local_text_embedding_calls']} local text embeddings, " + f"{resources['probe_elapsed_seconds']:.3f}s probe time, " + f"{resources['shot_detection_seconds']:.3f}s shot detection, " + f"{resources['codex_calls']} Codex calls." + ) + print(f"Full evidence: {result['output']}") + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Compare disjoint shot proposals on saved modality probes." + ) + parser.add_argument("task_id", nargs="?") + parser.add_argument("--held-out", action="store_true") + arguments = parser.parse_args() + if arguments.held_out == (arguments.task_id is not None): + parser.error("provide one task ID or --held-out") + if arguments.held_out: + _print_held_out(compare_held_out()) + else: + print( + json.dumps( + compare_shot_proposals(arguments.task_id), + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/scripts/sound_two_stage.py b/benchmarks/codex-mcp/scripts/sound_two_stage.py new file mode 100644 index 00000000..f7f94933 --- /dev/null +++ b/benchmarks/codex-mcp/scripts/sound_two_stage.py @@ -0,0 +1,392 @@ +from __future__ import annotations + +import hashlib +import json +import os +import platform +import subprocess +import time +from importlib.metadata import PackageNotFoundError, version +from pathlib import Path +from statistics import mean +from typing import Any + +from vidxp.application_models import ListMediaCommand, MediaState +from vidxp.benchmarks.agent_ablation_score import interval_iou +from vidxp.capabilities.search import search_embeddings +from vidxp.capabilities.sound.operations import ( + GLOBAL_REPRESENTATION, + LOCAL_REPRESENTATION, + REQUIRED_METADATA, + _activation_scope, + search_sound, + sound_embedding, +) +from vidxp.capabilities.sound.specs import FINELAP_MODEL +from vidxp.composition import create_local_application +from vidxp.search_fusion import fuse_search_results + +from modality_probe import ( + BENCHMARK_ROOT, + TASKS_PATH, + _load_environment, + _required_environment, +) +from query_routing_control import QUERY_PLAN_PATH + + +TOP_K = 3 + + +def _package_version(package: str) -> str | None: + try: + return version(package) + except PackageNotFoundError: + return None + + +def _git_state() -> dict[str, Any]: + repository = Path(__file__).resolve().parents[3] + + def run(*arguments: str) -> str: + return subprocess.check_output( + ("git", *arguments), + cwd=repository, + stderr=subprocess.DEVNULL, + text=True, + ).strip() + + try: + return { + "revision": run("rev-parse", "HEAD"), + "working_tree_dirty": bool(run("status", "--porcelain")), + } + except (OSError, subprocess.CalledProcessError): + return {"revision": None, "working_tree_dirty": None} + + +def _hit_metrics( + hits: tuple[Any, ...], expected_start: float, expected_end: float +) -> dict[str, Any]: + rows = [] + for hit in hits: + iou = interval_iou(hit.start, hit.end, expected_start, expected_end) + rows.append( + { + "rank": hit.rank, + "start_seconds": hit.start, + "end_seconds": hit.end, + "temporal_iou": iou, + "overlaps_target": iou > 0, + "raw_distance": hit.raw_distance, + "source_id": hit.source_id, + "window_index": hit.metadata.get("window_index"), + "activation_index": hit.metadata.get("activation_index"), + "context_rank": hit.metadata.get("context_rank"), + } + ) + first_overlap = next((row["rank"] for row in rows if row["overlaps_target"]), None) + return { + "target_covered": first_overlap is not None, + "first_overlap_rank": first_overlap, + "best_temporal_iou": max((row["temporal_iou"] for row in rows), default=0.0), + "hits": rows, + } + + +def _moment_metrics( + moment: Any, expected_start: float, expected_end: float +) -> dict[str, Any]: + if moment is None: + return { + "start_seconds": None, + "end_seconds": None, + "temporal_iou": 0.0, + "start_absolute_error_seconds": None, + "end_absolute_error_seconds": None, + "duration_absolute_error_seconds": None, + } + return { + "start_seconds": moment.start, + "end_seconds": moment.end, + "temporal_iou": interval_iou( + moment.start, moment.end, expected_start, expected_end + ), + "start_absolute_error_seconds": abs(moment.start - expected_start), + "end_absolute_error_seconds": abs(moment.end - expected_end), + "duration_absolute_error_seconds": abs( + (moment.end - moment.start) - (expected_end - expected_start) + ), + } + + +def run_benchmark() -> dict[str, Any]: + _load_environment() + tasks = json.loads(TASKS_PATH.read_text(encoding="utf-8")) + query_plan = json.loads(QUERY_PLAN_PATH.read_text(encoding="utf-8")) + held_out = [ + task + for task in tasks[2:] + if "sound" in task["modalities"] and task["id"] in query_plan["tasks"] + ] + context = create_local_application( + repository_name=os.environ.get("VIDXP_EVAL_REPOSITORY", "default"), + index_directory=_required_environment("VIDXP_EVAL_INDEX_DIR"), + data_directory=_required_environment("VIDXP_EVAL_DATA_DIR"), + device=os.environ.get("VIDXP_EVAL_DEVICE", "cpu"), + ) + application = context.application + config = application.index_backend.active_config( + application.index_directory, + device=application.device, + ) + records = [] + started = time.perf_counter() + + with application.index_backend.open_store(config) as storage: + with application.runtime.scheduler.inference(): + for task in held_out: + filename = Path(task["media_relpath"]).name + page = application.media.list( + ListMediaCommand( + page_size=2, + filename=filename, + state=MediaState.ready, + ) + ) + if len(page.items) != 1: + raise RuntimeError( + f"expected one ready media record for {filename}" + ) + media_id = page.items[0].media_id + query = str(task["query"]) + expected_start = float(task["expected_start"]) + expected_end = float(task["expected_end"]) + + product_started = time.perf_counter() + product = search_sound( + query, + config=config, + runtime=application.runtime, + top_k=TOP_K, + video_id=media_id, + storage=storage, + ) + product_elapsed = time.perf_counter() - product_started + fused = fuse_search_results( + query=query, + requested_modalities=("sound",), + results=(product,), + media_id=media_id, + top_k=TOP_K, + snapshot_id=config.snapshot_id, + ) + + diagnostic_started = time.perf_counter() + embedding = sound_embedding(query, application.runtime) + windows = search_embeddings( + query, + "sound", + embedding, + config=config, + required_metadata=REQUIRED_METADATA, + top_k=TOP_K, + video_id=media_id, + filters={"representation": GLOBAL_REPRESENTATION}, + storage=storage, + ) + activation_scope = _activation_scope(windows.hits, video_id=media_id) + activation_count = storage.count_records( + "sound", video_id=media_id, filters=activation_scope + ) + gated_activations = search_embeddings( + query, + "sound", + embedding, + config=config, + required_metadata=REQUIRED_METADATA, + top_k=max(1, activation_count), + video_id=media_id, + filters=activation_scope, + storage=storage, + ) + diagnostic_elapsed = time.perf_counter() - diagnostic_started + if [hit.source_id for hit in product.hits] != [ + hit.source_id for hit in gated_activations.hits[:TOP_K] + ]: + raise RuntimeError( + f"product and diagnostic rankings differ for {task['id']}" + ) + + records.append( + { + "task_id": task["id"], + "video_file": filename, + "query": query, + "expected": { + "start_seconds": expected_start, + "end_seconds": expected_end, + }, + "global_gate_top3": _hit_metrics( + windows.hits, expected_start, expected_end + ), + "product_activation_top3": _hit_metrics( + product.hits, expected_start, expected_end + ), + "gated_activation_full_ranking": _hit_metrics( + gated_activations.hits, expected_start, expected_end + ), + "final_sound_only_top1": _moment_metrics( + fused.moments[0] if fused.moments else None, + expected_start, + expected_end, + ), + "counts": { + "gated_activation_records": activation_count, + "product_text_embeddings": 1, + "product_vector_queries": 2, + "diagnostic_text_embeddings": 1, + "diagnostic_vector_queries": 2, + }, + "runtime_seconds": { + "product": product_elapsed, + "diagnostic": diagnostic_elapsed, + }, + } + ) + + final = [record["final_sound_only_top1"] for record in records] + count = len(records) + summary = { + "task_count": count, + "global_gate_top3_coverage": sum( + record["global_gate_top3"]["target_covered"] for record in records + ) + / count, + "product_activation_top1_coverage": sum( + record["product_activation_top3"]["hits"][0]["overlaps_target"] + for record in records + ) + / count, + "product_activation_top3_coverage": sum( + record["product_activation_top3"]["target_covered"] for record in records + ) + / count, + "full_gated_activation_coverage": sum( + record["gated_activation_full_ranking"]["target_covered"] + for record in records + ) + / count, + "mean_final_top1_temporal_iou": mean(item["temporal_iou"] for item in final), + "final_top1_recall_at_iou_0_3": ( + sum(item["temporal_iou"] >= 0.3 for item in final) / count + ), + "final_top1_recall_at_iou_0_5": ( + sum(item["temporal_iou"] >= 0.5 for item in final) / count + ), + "final_top1_recall_at_iou_0_7": ( + sum(item["temporal_iou"] >= 0.7 for item in final) / count + ), + "mean_start_absolute_error_seconds": mean( + item["start_absolute_error_seconds"] for item in final + ), + "mean_end_absolute_error_seconds": mean( + item["end_absolute_error_seconds"] for item in final + ), + "mean_duration_absolute_error_seconds": mean( + item["duration_absolute_error_seconds"] for item in final + ), + "total_runtime_seconds": time.perf_counter() - started, + } + return { + "schema_version": 1, + "benchmark": "finelap-two-stage-held-out", + "scope": { + "task_manifest": str(TASKS_PATH.relative_to(BENCHMARK_ROOT)), + "task_manifest_sha256": hashlib.sha256(TASKS_PATH.read_bytes()).hexdigest(), + "split": "frozen pilot tasks 3-10; sound-tagged tasks only", + "query_input": "full frozen application query; no rewrite", + "top_k": TOP_K, + }, + "system": { + "sound_model": FINELAP_MODEL.identity(), + "global_representation": GLOBAL_REPRESENTATION, + "local_representation": LOCAL_REPRESENTATION, + "snapshot_id": config.snapshot_id, + "vector_distance": config.vector_distance, + "device": str(application.device), + "git": _git_state(), + "machine": { + "platform": platform.platform(), + "architecture": platform.machine(), + "python": platform.python_version(), + "packages": { + package: _package_version(package) + for package in ("torch", "transformers", "chromadb", "numpy") + }, + }, + }, + "measurement": { + "product_path_per_task": ( + "one text embedding, global top-3 gate, local top-3 activation ranking" + ), + "diagnostic_overhead_per_task": ( + "one extra text embedding and two vector queries expose the complete " + "gated activation ranking" + ), + "final_interval": "top sound-only moment after production fusion", + }, + "summary": summary, + "tasks": records, + } + + +def main() -> int: + report = run_benchmark() + data_directory = Path(_required_environment("VIDXP_EVAL_DATA_DIR")) + output = data_directory.parent / "localization" / "sound-two-stage-held-out.json" + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + summary = report["summary"] + print("FineLAP two-stage held-out status") + print(f"Tasks: {summary['task_count']} (full frozen queries; top_k={TOP_K})") + print( + "Coverage: " + f"global gate {summary['global_gate_top3_coverage']:.0%}, " + f"activation top-1 {summary['product_activation_top1_coverage']:.0%}, " + f"activation top-3 {summary['product_activation_top3_coverage']:.0%}, " + f"full gated list {summary['full_gated_activation_coverage']:.0%}" + ) + print( + "Final top-1: " + f"mean IoU {summary['mean_final_top1_temporal_iou']:.4f}; " + f"R@0.3 {summary['final_top1_recall_at_iou_0_3']:.0%}; " + f"R@0.5 {summary['final_top1_recall_at_iou_0_5']:.0%}; " + f"R@0.7 {summary['final_top1_recall_at_iou_0_7']:.0%}" + ) + print("Per task:") + for record in report["tasks"]: + final = record["final_sound_only_top1"] + gated = record["gated_activation_full_ranking"] + gate_status = ( + "hit" if record["global_gate_top3"]["target_covered"] else "miss" + ) + top3_status = ( + "hit" if record["product_activation_top3"]["target_covered"] else "miss" + ) + print( + f"- {record['task_id']}: " + f"gate={gate_status}, " + f"top3={top3_status}, " + f"first gated target rank={gated['first_overlap_rank'] or 'none'}, " + f"final={final['start_seconds']:.3f}-{final['end_seconds']:.3f}, " + f"IoU={final['temporal_iou']:.4f}" + ) + print(f"Report: {output}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/codex-mcp/tasks/longvale-part9-modality-queries.json b/benchmarks/codex-mcp/tasks/longvale-part9-modality-queries.json new file mode 100644 index 00000000..81bbdb11 --- /dev/null +++ b/benchmarks/codex-mcp/tasks/longvale-part9-modality-queries.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "method": "manual_modality_query_ceiling_v1", + "constraints": [ + "derive each phrase only from the task query's stated content", + "do not use timestamps, retrieved results, or video inspection", + "retain only the words relevant to the named modality" + ], + "tasks": { + "longvale-part9-ZId-car-siren": { + "action": "a red car speeds down a winding road", + "sound": "a siren suddenly blares" + }, + "longvale-part9-ZId-engine-rev": { + "action": "the driver gestures", + "sound": "Cayenne Coupe engine revving and exhaust sound" + }, + "longvale-part9-ZId-sketch": { + "scene": "a hand among automotive drawings", + "action": "a hand sketches the sleek lines of a car" + }, + "longvale-part9-ZGX-office-speech": { + "scene": "Changlin Dou sits at his office desk", + "speech": "bringing innovative medicine to the Chinese market" + }, + "longvale-part9-py-signing": { + "scene": "a woman against a blue dotted background", + "action": "a woman signs the phrase Find words you know" + }, + "longvale-part9-py-phone-ring": { + "scene": "Website coming in 2018 appears in purple letters", + "sound": "a telephone rings" + }, + "longvale-part9-ZVU-stir-and-cover": { + "scene": "chicken casserole in a green pot", + "action": "a hand stirs chicken casserole and secures the lid" + }, + "longvale-part9-ZVU-casserole-drumbeat": { + "scene": "a close-up of completed chicken casserole", + "sound": "a simple drumbeat plays" + } + } +} diff --git a/benchmarks/codex-mcp/tasks/longvale-part9-pilot.json b/benchmarks/codex-mcp/tasks/longvale-part9-pilot.json index faaa84aa..a20d6731 100644 --- a/benchmarks/codex-mcp/tasks/longvale-part9-pilot.json +++ b/benchmarks/codex-mcp/tasks/longvale-part9-pilot.json @@ -4,7 +4,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZYTmgi1pAIE", "media_relpath": "media/ZYTmgi1pAIE.mp4", - "duration_seconds": 75.809067, + "duration_seconds": 75.813152, "event_index": 0, "query": "heavy rain and howling wind over a desolate landscape, followed by an engine starting and revving", "expected_start": 0.0, @@ -16,7 +16,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZYTmgi1pAIE", "media_relpath": "media/ZYTmgi1pAIE.mp4", - "duration_seconds": 75.809067, + "duration_seconds": 75.813152, "event_index": 2, "query": "a ringing bell echoes while SUBSCRIBE flashes over a starry night sky", "expected_start": 70.07, @@ -28,7 +28,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZIdFAGJrlCw", "media_relpath": "media/ZIdFAGJrlCw.mp4", - "duration_seconds": 296.4, + "duration_seconds": 296.402721, "event_index": 0, "query": "a red car speeds down a winding road as a siren suddenly blares", "expected_start": 7.68, @@ -40,7 +40,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZIdFAGJrlCw", "media_relpath": "media/ZIdFAGJrlCw.mp4", - "duration_seconds": 296.4, + "duration_seconds": 296.402721, "event_index": 3, "query": "the driver gestures while revving the Cayenne Coupe engine to demonstrate its exhaust sound", "expected_start": 25.56, @@ -52,7 +52,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZIdFAGJrlCw", "media_relpath": "media/ZIdFAGJrlCw.mp4", - "duration_seconds": 296.4, + "duration_seconds": 296.402721, "event_index": 8, "query": "a hand sketches the sleek lines of a car among other automotive drawings", "expected_start": 88.8, @@ -64,7 +64,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZGXCr5n8Frg", "media_relpath": "media/ZGXCr5n8Frg.mp4", - "duration_seconds": 222.28, + "duration_seconds": 222.284626, "event_index": 2, "query": "Changlin Dou sits at his office desk and describes bringing innovative medicine to the Chinese market", "expected_start": 22.24, @@ -76,7 +76,7 @@ "dataset": "LongVALE evaluation", "video_id": "_py1WXVX4oc", "media_relpath": "media/_py1WXVX4oc.mp4", - "duration_seconds": 73.139733, + "duration_seconds": 73.142857, "event_index": 2, "query": "a woman signs the phrase Find words you know against a blue dotted background", "expected_start": 9.509, @@ -88,7 +88,7 @@ "dataset": "LongVALE evaluation", "video_id": "_py1WXVX4oc", "media_relpath": "media/_py1WXVX4oc.mp4", - "duration_seconds": 73.139733, + "duration_seconds": 73.142857, "event_index": 4, "query": "Website coming in 2018 appears in purple letters while a telephone rings", "expected_start": 70.136, @@ -100,7 +100,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZVUAC3m48G0", "media_relpath": "media/ZVUAC3m48G0.mp4", - "duration_seconds": 247.16, + "duration_seconds": 247.176417, "event_index": 2, "query": "a hand stirs chicken casserole in a green pot and secures the lid", "expected_start": 190.24, @@ -112,7 +112,7 @@ "dataset": "LongVALE evaluation", "video_id": "ZVUAC3m48G0", "media_relpath": "media/ZVUAC3m48G0.mp4", - "duration_seconds": 247.16, + "duration_seconds": 247.176417, "event_index": 4, "query": "a close-up shows the completed chicken casserole as a simple drumbeat plays", "expected_start": 242.88, diff --git a/desktop/THIRD_PARTY_NOTICES.txt b/desktop/THIRD_PARTY_NOTICES.txt index b6aed2e0..5d15ff5d 100644 --- a/desktop/THIRD_PARTY_NOTICES.txt +++ b/desktop/THIRD_PARTY_NOTICES.txt @@ -4925,7 +4925,6 @@ Used by: - event-listener-strategy 0.5.4 | https://github.com/smol-rs/event-listener-strategy | registry+https://github.com/rust-lang/crates.io-index - event-listener 5.4.2 | https://github.com/smol-rs/event-listener | registry+https://github.com/rust-lang/crates.io-index - fastrand 2.5.0 | https://github.com/smol-rs/fastrand | registry+https://github.com/rust-lang/crates.io-index -- filetime 0.2.29 | https://github.com/alexcrichton/filetime | registry+https://github.com/rust-lang/crates.io-index - flate2 1.1.10 | https://github.com/rust-lang/flate2-rs | registry+https://github.com/rust-lang/crates.io-index - fnv 1.0.7 | https://github.com/servo/rust-fnv | registry+https://github.com/rust-lang/crates.io-index - form_urlencoded 1.2.2 | https://github.com/servo/rust-url | registry+https://github.com/rust-lang/crates.io-index @@ -4984,7 +4983,6 @@ Used by: - system-configuration-sys 0.6.0 | https://github.com/mullvad/system-configuration-rs | registry+https://github.com/rust-lang/crates.io-index - system-configuration 0.7.0 | https://github.com/mullvad/system-configuration-rs | registry+https://github.com/rust-lang/crates.io-index - tao-macros 0.1.4 | https://github.com/tauri-apps/tao | registry+https://github.com/rust-lang/crates.io-index -- tar 0.4.46 | https://github.com/composefs/tar-rs | registry+https://github.com/rust-lang/crates.io-index - tempfile 3.27.0 | https://github.com/Stebalien/tempfile | registry+https://github.com/rust-lang/crates.io-index - tendril 0.5.1 | https://github.com/servo/html5ever | registry+https://github.com/rust-lang/crates.io-index - toml_datetime 0.6.3 | https://github.com/toml-rs/toml | registry+https://github.com/rust-lang/crates.io-index @@ -5003,7 +5001,6 @@ Used by: - window-vibrancy 0.6.0 | https://github.com/tauri-apps/tauri-plugin-vibrancy | registry+https://github.com/rust-lang/crates.io-index - wit-bindgen 0.46.0 | https://github.com/bytecodealliance/wit-bindgen | registry+https://github.com/rust-lang/crates.io-index - wry 0.55.1 | https://github.com/tauri-apps/wry | registry+https://github.com/rust-lang/crates.io-index -- xattr 1.6.1 | https://github.com/Stebalien/xattr | registry+https://github.com/rust-lang/crates.io-index Apache License Version 2.0, January 2004 @@ -8001,7 +7998,6 @@ Used by: - time-core 0.1.8 | https://github.com/time-rs/time | registry+https://github.com/rust-lang/crates.io-index - time-macros 0.2.27 | https://github.com/time-rs/time | registry+https://github.com/rust-lang/crates.io-index - time 0.3.47 | https://github.com/time-rs/time | registry+https://github.com/rust-lang/crates.io-index -- typed-path 0.12.3 | https://github.com/chipsenkbeil/typed-path | registry+https://github.com/rust-lang/crates.io-index - typeid 1.0.3 | https://github.com/dtolnay/typeid | registry+https://github.com/rust-lang/crates.io-index - unic-char-property 0.9.0 | https://github.com/open-i18n/rust-unic/ | registry+https://github.com/rust-lang/crates.io-index - unic-char-range 0.9.0 | https://github.com/open-i18n/rust-unic/ | registry+https://github.com/rust-lang/crates.io-index @@ -9581,35 +9577,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -------------------------------------------------------------------------------- -License: MIT License (MIT) -Used by: -- zip 7.2.0 | https://github.com/zip-rs/zip2.git | registry+https://github.com/rust-lang/crates.io-index - -The MIT License (MIT) - -Copyright (c) 2014 Mathijs van de Nes - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. - -Some files in the "tests/data" subdirectory of this repository are under other -licences; see files named LICENSE.*.txt for details. ------------------------------------------------------------------------------- License: MIT License (MIT) Used by: @@ -11231,31 +11198,6 @@ SPDX-License-Identifier: Unicode-3.0 Portions of ICU4X may have been adapted from ICU4C and/or ICU4J. ICU 1.8.1 to ICU 57.1 Ā© 1995-2016 International Business Machines Corporation and others. -------------------------------------------------------------------------------- -License: zlib License (Zlib) -Used by: -- zlib-rs 0.6.7 | https://github.com/trifectatechfoundation/zlib-rs | registry+https://github.com/rust-lang/crates.io-index - -(C) 2024 Trifecta Tech Foundation - -This software is provided 'as-is', without any express or implied -warranty. In no event will the authors be held liable for any damages -arising from the use of this software. - -Permission is granted to anyone to use this software for any purpose, -including commercial applications, and to alter it and redistribute it -freely, subject to the following restrictions: - -1. The origin of this software must not be misrepresented; you must not - claim that you wrote the original software. If you use this software - in a product, an acknowledgment in the product documentation would be - appreciated but is not required. - -2. Altered source versions must be plainly marked as such, and must not be - misrepresented as being the original software. - -3. This notice may not be removed or altered from any source distribution. - ------------------------------------------------------------------------------- License: zlib License (Zlib) Used by: diff --git a/desktop/capability-catalog.json b/desktop/capability-catalog.json deleted file mode 100644 index 8297ae0d..00000000 --- a/desktop/capability-catalog.json +++ /dev/null @@ -1,85 +0,0 @@ -{ - "schema_version": 1, - "capabilities": { - "speech": { - "extra": "speech", - "modality": "speech", - "label": "Speech search", - "description": "Transcribe and search spoken words with timestamps.", - "models": [ - { - "cache_key": "models--Qwen--Qwen3-Embedding-0.6B/snapshots/97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3/model.safetensors", - "download_size_bytes": 1207489041 - }, - { - "cache_key": "models--dropbox-dash--faster-whisper-large-v3-turbo/snapshots/0a363e9161cbc7ed1431c9597a8ceaf0c4f78fcf/model.bin", - "download_size_bytes": 1621668947 - } - ] - }, - "sound": { - "extra": "sound", - "modality": "sound", - "label": "Sound event search", - "description": "Index and search music, environmental sounds, and audio events.", - "models": [ - { - "cache_key": "finelap-tokenizer/config.json", - "download_size_bytes": 481 - }, - { - "cache_key": "finelap-tokenizer/merges.txt", - "download_size_bytes": 456318 - }, - { - "cache_key": "finelap-tokenizer/vocab.json", - "download_size_bytes": 898823 - }, - { - "cache_key": "models--AndreasXi--FineLAP/snapshots/b419aa22947d29907a5567f21b81bf3b39a40449/model.safetensors", - "download_size_bytes": 980404741 - } - ] - }, - "scene": { - "extra": "scene", - "modality": "scene", - "label": "Visual scene search", - "description": "Index and search visual scenes.", - "models": [ - { - "cache_key": "models--google--siglip2-base-patch16-224/snapshots/75de2d55ec2d0b4efc50b3e9ad70dba96a7b2fa2/model.safetensors", - "download_size_bytes": 1539458338 - } - ] - }, - "actor": { - "extra": "actor", - "modality": "actor", - "label": "Actor recognition", - "description": "Index, inspect, and render actor clusters.", - "models": [ - { - "cache_key": "opencv-zoo/face_detection_yunet_2026may.onnx", - "download_size_bytes": 229738 - }, - { - "cache_key": "opencv-zoo/face_recognition_sface_2021dec.onnx", - "download_size_bytes": 38696353 - } - ] - }, - "action": { - "extra": "action", - "modality": "action", - "label": "Action and motion search", - "description": "Index and search multi-frame actions and motion.", - "models": [ - { - "cache_key": "models--google--videoprism-lvt-base-f16r288/snapshots/fb6de9f0eb7bc285be86bdca1cf7daa3e3ef51ff/model.safetensors", - "download_size_bytes": 993993146 - } - ] - } - } -} diff --git a/desktop/model-cache-catalog.json b/desktop/model-cache-catalog.json deleted file mode 100644 index 49590f9b..00000000 --- a/desktop/model-cache-catalog.json +++ /dev/null @@ -1,52 +0,0 @@ -[ - { - "id": "AndreasXi/FineLAP", - "label": "AndreasXi/FineLAP", - "relative_artifact": "models--AndreasXi--FineLAP/snapshots/b419aa22947d29907a5567f21b81bf3b39a40449/model.safetensors" - }, - { - "id": "dropbox-dash/faster-whisper-large-v3-turbo", - "label": "dropbox-dash/faster-whisper-large-v3-turbo", - "relative_artifact": "models--dropbox-dash--faster-whisper-large-v3-turbo/snapshots/0a363e9161cbc7ed1431c9597a8ceaf0c4f78fcf/model.bin" - }, - { - "id": "FacebookAI/roberta-base config", - "label": "FacebookAI/roberta-base config", - "relative_artifact": "finelap-tokenizer/config.json" - }, - { - "id": "FacebookAI/roberta-base merges", - "label": "FacebookAI/roberta-base merges", - "relative_artifact": "finelap-tokenizer/merges.txt" - }, - { - "id": "FacebookAI/roberta-base vocab", - "label": "FacebookAI/roberta-base vocab", - "relative_artifact": "finelap-tokenizer/vocab.json" - }, - { - "id": "google/siglip2-base-patch16-224", - "label": "google/siglip2-base-patch16-224", - "relative_artifact": "models--google--siglip2-base-patch16-224/snapshots/75de2d55ec2d0b4efc50b3e9ad70dba96a7b2fa2/model.safetensors" - }, - { - "id": "google/videoprism-lvt-base-f16r288", - "label": "google/videoprism-lvt-base-f16r288", - "relative_artifact": "models--google--videoprism-lvt-base-f16r288/snapshots/fb6de9f0eb7bc285be86bdca1cf7daa3e3ef51ff/model.safetensors" - }, - { - "id": "Qwen/Qwen3-Embedding-0.6B", - "label": "Qwen/Qwen3-Embedding-0.6B", - "relative_artifact": "models--Qwen--Qwen3-Embedding-0.6B/snapshots/97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3/model.safetensors" - }, - { - "id": "sface", - "label": "sface", - "relative_artifact": "opencv-zoo/face_recognition_sface_2021dec.onnx" - }, - { - "id": "yunet", - "label": "yunet", - "relative_artifact": "opencv-zoo/face_detection_yunet_2026may.onnx" - } -] diff --git a/desktop/package.json b/desktop/package.json index f6a3004f..3d169408 100644 --- a/desktop/package.json +++ b/desktop/package.json @@ -5,7 +5,7 @@ "type": "module", "scripts": { "dev": "vite", - "prebuild": "npm run sync:branding", + "prebuild": "npm run sync:generated", "build": "tsc -b && vite build", "typecheck": "tsc -b --pretty false", "lint": "eslint . --max-warnings 0", @@ -13,12 +13,12 @@ "check": "npm run typecheck && npm run lint && npm run test && npm run build", "notices:write": "node scripts/generate-notices.mjs --write", "notices:check": "node scripts/generate-notices.mjs", - "model-catalog:write": "uv run --frozen python scripts/model-catalog.py --write", - "model-catalog:check": "uv run --frozen python scripts/model-catalog.py --check", + "model-catalog:generate": "uv run --frozen python scripts/model-catalog.py", "tauri": "tauri", "sync:branding": "node scripts/sync-branding.mjs", + "sync:generated": "npm run sync:branding && npm run model-catalog:generate", "icons": "tauri icon ../docs/images/logo.png --output src-tauri/icons && npm run sync:branding", - "predesktop:dev": "npm run sync:branding", + "predesktop:dev": "npm run sync:generated", "desktop:dev": "tauri dev", "predesktop:build": "npm run icons", "desktop:build": "node scripts/build-desktop.mjs", diff --git a/desktop/runtime-manifest.json b/desktop/runtime-manifest.json index 1937d5b2..6a2b7583 100644 --- a/desktop/runtime-manifest.json +++ b/desktop/runtime-manifest.json @@ -7,33 +7,6 @@ "python_version": "3.14.6", "uv_version": "0.12.0", "managed_runtime_estimated_size_bytes": 3221225472, - "local_answers": { - "engine": "ollama", - "model": "qwen3.5:4b-q4_K_M", - "download_size_bytes": 3650722202, - "managed_runtime": { - "version": "0.32.5", - "maximum_download_size_bytes": 1457824795, - "artifacts": { - "windows-x86_64": { - "url": "https://github.com/ollama/ollama/releases/download/v0.32.5/ollama-windows-amd64.zip", - "sha256": "7c941ae084569d298062d29f8139163a3187c76dbca0479c70d085e78fd8c7bb", - "download_size_bytes": 1457824795, - "archive": "zip", - "executable": "ollama.exe" - }, - "macos-aarch64": { - "url": "https://github.com/ollama/ollama/releases/download/v0.32.5/ollama-darwin.tgz", - "sha256": "5789dd037a86adb328c72c11fc45e6c558452d07e5b50814a8bdb7b0fbdbcd81", - "download_size_bytes": 145747028, - "archive": "tar_gz", - "executable": "ollama" - } - } - }, - "label": "Local grounded answers", - "description": "Turn VidXP search evidence into cited answers on this computer. Setup reuses Ollama when available or downloads a private headless runtime and the approved Qwen 3.5 4B model." - }, "surfaces": { "worker": { "extra": "local-worker", diff --git a/desktop/scripts/model-catalog.py b/desktop/scripts/model-catalog.py index e48ad832..5940666f 100644 --- a/desktop/scripts/model-catalog.py +++ b/desktop/scripts/model-catalog.py @@ -1,6 +1,5 @@ from __future__ import annotations -import argparse import json import sys from pathlib import Path @@ -15,9 +14,10 @@ ) +GENERATED = ROOT / "desktop" / "generated" CATALOGS = { - ROOT / "desktop" / "capability-catalog.json": desktop_capability_catalog, - ROOT / "desktop" / "model-cache-catalog.json": desktop_model_cache_catalog, + GENERATED / "capability-catalog.json": desktop_capability_catalog, + GENERATED / "model-cache-catalog.json": desktop_model_cache_catalog, } @@ -30,29 +30,12 @@ def rendered_catalog(value: object) -> str: def main() -> int: - parser = argparse.ArgumentParser() - mode = parser.add_mutually_exclusive_group(required=True) - mode.add_argument("--write", action="store_true") - mode.add_argument("--check", action="store_true") - arguments = parser.parse_args() - if arguments.write: - for path, derive in CATALOGS.items(): - path.write_text( - rendered_catalog(derive()), - encoding="utf-8", - newline="\n", - ) - return 0 - stale = [ - path.relative_to(ROOT).as_posix() - for path, derive in CATALOGS.items() - if not path.exists() - or path.read_text(encoding="utf-8") != rendered_catalog(derive()) - ] - if stale: - raise SystemExit( - f"{', '.join(stale)} is stale; run npm run " - "model-catalog:write from desktop/." + GENERATED.mkdir(parents=True, exist_ok=True) + for path, derive in CATALOGS.items(): + path.write_text( + rendered_catalog(derive()), + encoding="utf-8", + newline="\n", ) return 0 diff --git a/desktop/src-tauri/Cargo.lock b/desktop/src-tauri/Cargo.lock index 7157c50b..27a7780c 100644 --- a/desktop/src-tauri/Cargo.lock +++ b/desktop/src-tauri/Cargo.lock @@ -1114,16 +1114,6 @@ dependencies = [ "rustc_version", ] -[[package]] -name = "filetime" -version = "0.2.29" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5c287a33c7f0a620c38e641e7f60827713987b3c0f26e8ddc9462cc69cf75759" -dependencies = [ - "cfg-if", - "libc", -] - [[package]] name = "find-msvc-tools" version = "0.1.9" @@ -3947,17 +3937,6 @@ dependencies = [ "syn 2.0.119", ] -[[package]] -name = "tar" -version = "0.4.46" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3f6221d9a6003c78398e3b239969f352578258df48c8eb051caadae0015bc840" -dependencies = [ - "filetime", - "libc", - "xattr", -] - [[package]] name = "target-lexicon" version = "0.12.16" @@ -4724,12 +4703,6 @@ version = "0.2.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e421abadd41a4225275504ea4d6566923418b7f05506fbc9c0fe86ba7396114b" -[[package]] -name = "typed-path" -version = "0.12.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8e28f89b80c87b8fb0cf04ab448d5dd0dd0ade2f8891bae878de66a75a28600e" - [[package]] name = "typeid" version = "1.0.3" @@ -4872,7 +4845,6 @@ name = "vidxp-desktop" version = "0.4.0" dependencies = [ "atomic-write-file", - "flate2", "hex", "log", "process-wrap", @@ -4880,7 +4852,6 @@ dependencies = [ "serde", "serde_json", "sha2 0.11.0", - "tar", "tauri", "tauri-build", "tauri-plugin-dialog", @@ -4891,7 +4862,6 @@ dependencies = [ "tauri-plugin-store", "which", "windows 0.62.2", - "zip", ] [[package]] @@ -5756,16 +5726,6 @@ dependencies = [ "pkg-config", ] -[[package]] -name = "xattr" -version = "1.6.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "32e45ad4206f6d2479085147f02bc2ef834ac85886624a23575ae137c8aa8156" -dependencies = [ - "libc", - "rustix", -] - [[package]] name = "yoke" version = "0.8.3" @@ -5930,19 +5890,6 @@ dependencies = [ "syn 2.0.119", ] -[[package]] -name = "zip" -version = "7.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c42e33efc22a0650c311c2ef19115ce232583abbe80850bc8b66509ebef02de0" -dependencies = [ - "crc32fast", - "flate2", - "indexmap 2.14.0", - "memchr", - "typed-path", -] - [[package]] name = "zlib-rs" version = "0.6.7" diff --git a/desktop/src-tauri/Cargo.toml b/desktop/src-tauri/Cargo.toml index e954e2bd..cbb5c94d 100644 --- a/desktop/src-tauri/Cargo.toml +++ b/desktop/src-tauri/Cargo.toml @@ -17,7 +17,6 @@ tauri-build = { version = "2.6.3", features = [] } [dependencies] atomic-write-file = "0.3.0" -flate2 = "1.1.10" hex = "0.4.3" log = "0.4.29" process-wrap = { version = "9.1.0", features = ["std"] } @@ -25,7 +24,6 @@ reqwest = { version = "0.13.4", default-features = false, features = ["blocking" serde = { version = "1.0.229", features = ["derive"] } serde_json = "1.0.151" sha2 = "0.11.0" -tar = "0.4.46" tauri = { version = "2.11.5", features = ["tray-icon"] } tauri-plugin-log = "2.9.0" tauri-plugin-dialog = "2.7.2" @@ -34,7 +32,6 @@ tauri-plugin-shell = "2.3.5" tauri-plugin-single-instance = "2.4.3" tauri-plugin-store = "2.4.4" which = "8.0.0" -zip = { version = "7.2.0", default-features = false, features = ["deflate-flate2-zlib-rs"] } [target.'cfg(windows)'.dependencies] windows = { version = "0.62.2", features = ["Win32_System_Threading"] } diff --git a/desktop/src-tauri/build.rs b/desktop/src-tauri/build.rs index 174f6707..71441bea 100644 --- a/desktop/src-tauri/build.rs +++ b/desktop/src-tauri/build.rs @@ -7,8 +7,11 @@ fn main() { serde_json::from_slice(include_bytes!("../runtime-manifest.json")) .expect("desktop/runtime-manifest.json must be valid JSON"); let capability_catalog: serde_json::Value = - serde_json::from_slice(include_bytes!("../capability-catalog.json")) - .expect("desktop/capability-catalog.json must be valid JSON"); + serde_json::from_slice(include_bytes!("../generated/capability-catalog.json")) + .expect("the generated desktop capability catalog must be valid JSON"); + let local_answers: serde_json::Value = + serde_json::from_slice(include_bytes!("../../src/vidxp/assets/local-answers.json")) + .expect("src/vidxp/assets/local-answers.json must be valid JSON"); assert_eq!( capability_catalog["schema_version"].as_u64(), Some(1), @@ -20,6 +23,7 @@ fn main() { .expect("desktop capability catalog must contain capabilities") .clone(); manifest["capabilities"] = serde_json::Value::Object(capabilities); + manifest["local_answers"] = local_answers; let expected = manifest["uv_version"] .as_str() .expect("runtime manifest must contain uv_version"); @@ -166,7 +170,8 @@ fn main() { println!("cargo:rerun-if-changed=../../uv.lock"); println!("cargo:rerun-if-changed=../../dist"); println!("cargo:rerun-if-changed=../runtime-manifest.json"); - println!("cargo:rerun-if-changed=../capability-catalog.json"); + println!("cargo:rerun-if-changed=../generated/capability-catalog.json"); + println!("cargo:rerun-if-changed=../../src/vidxp/assets/local-answers.json"); let attributes = tauri_build::Attributes::new(); #[cfg(windows)] diff --git a/desktop/src-tauri/src/lib.rs b/desktop/src-tauri/src/lib.rs index 0e897430..31d9ee3b 100644 --- a/desktop/src-tauri/src/lib.rs +++ b/desktop/src-tauri/src/lib.rs @@ -2,7 +2,7 @@ use std::{ borrow::Cow, collections::{BTreeMap, BTreeSet}, env, fs, - io::{self, BufRead, BufReader, Read, Write}, + io::{self, Read, Write}, net::{SocketAddr, TcpListener, TcpStream}, path::{Path, PathBuf}, process::Command, @@ -52,14 +52,13 @@ const RUNTIME_PACKAGE_WHEEL_NAME: &str = include_str!(concat!(env!("OUT_DIR"), "/runtime-package-name.txt")); const RUNTIME_PACKAGE_WHEEL_SHA256: &str = include_str!(concat!(env!("OUT_DIR"), "/runtime-package-sha256.txt")); -const MODEL_CACHE_CATALOG_BYTES: &[u8] = include_bytes!("../../model-cache-catalog.json"); +const MODEL_CACHE_CATALOG_BYTES: &[u8] = include_bytes!("../../generated/model-cache-catalog.json"); const CODEX_PLUGIN_MARKETPLACE_SOURCE: &str = "grayhatdevelopers/vidxp"; const CODEX_PLUGIN_MARKETPLACE_REF: Option<&str> = option_env!("VIDXP_PLUGIN_MARKETPLACE_REF"); const PRODUCT_DATA_DIRECTORY_NAME: &str = "VidXP"; const RUNTIME_CONSTRAINTS_FILE_NAME: &str = "runtime-constraints.txt"; const MAX_SETUP_OUTPUT_BYTES: usize = 4 * 1024 * 1024; const MEDIA_RUNTIME_INSTALL_TIMEOUT: Duration = Duration::from_secs(15 * 60); -const QUERY_MODEL_PULL_TIMEOUT: Duration = Duration::from_secs(2 * 60 * 60); static READINESS_SEQUENCE: AtomicU64 = AtomicU64::new(0); #[derive(Clone, Deserialize, Serialize)] @@ -324,31 +323,6 @@ struct OllamaVersionResponse { version: String, } -#[derive(Deserialize)] -struct OllamaTagsResponse { - models: Vec, -} - -#[derive(Deserialize)] -struct OllamaModel { - name: String, - #[serde(default)] - digest: String, -} - -#[derive(Deserialize)] -struct OllamaPullProgress { - status: String, - #[serde(default)] - digest: Option, - #[serde(default)] - total: Option, - #[serde(default)] - completed: Option, - #[serde(default)] - error: Option, -} - #[derive(Clone)] struct DesktopPaths { private_data: PathBuf, @@ -1651,10 +1625,6 @@ fn ollama_management_url(path: &str) -> String { format!("http://{}{path}", query_setup::OLLAMA_HOST) } -fn human_bytes(bytes: u64) -> String { - const GIB: f64 = 1024.0 * 1024.0 * 1024.0; - format!("{:.2} GiB", bytes as f64 / GIB) -} fn ollama_client(timeout: Duration) -> Result { reqwest::blocking::Client::builder() @@ -1680,20 +1650,6 @@ fn ollama_server_version() -> Result { Ok(response.version) } -fn installed_ollama_model(model: &str) -> Result, String> { - let response = ollama_client(Duration::from_secs(10))? - .get(ollama_management_url("/api/tags")) - .send() - .and_then(reqwest::blocking::Response::error_for_status) - .map_err(|error| format!("Could not inspect local Ollama models: {error}"))? - .json::() - .map_err(|error| format!("Ollama returned an invalid model inventory: {error}"))?; - Ok(response.models.into_iter().find(|candidate| { - candidate.name == model - || candidate.name.strip_suffix(":latest") == model.strip_suffix(":latest") - })) -} - fn stop_query_process(state: &DesktopState) { let Ok(mut active) = state.query_process.lock() else { return; @@ -1770,217 +1726,7 @@ fn ensure_query_service( Ok(version) } -fn pull_ollama_model( - app: &AppHandle, - draft_id: &str, - current: u8, - total_steps: u8, - model: &str, - cancellation: background_process::CancellationToken, -) -> Result { - if let Some(installed) = installed_ollama_model(model)? { - emit_local_answer_progress( - app, - draft_id, - current, - total_steps, - format!("Reusing {model}"), - None, - None, - ); - return Ok(installed); - } - let response = ollama_client(QUERY_MODEL_PULL_TIMEOUT)? - .post(ollama_management_url("/api/pull")) - .json(&serde_json::json!({"model": model, "stream": true})) - .send() - .and_then(reqwest::blocking::Response::error_for_status) - .map_err(|error| format!("Could not start the {model} download: {error}"))?; - let reader = BufReader::new(response); - for line in reader.lines() { - if cancellation.is_cancelled() { - return Err("the local answer model download was cancelled".into()); - } - let line = line.map_err(|error| format!("The model download stream failed: {error}"))?; - if line.trim().is_empty() { - continue; - } - let progress: OllamaPullProgress = serde_json::from_str(&line) - .map_err(|error| format!("Ollama returned invalid download progress: {error}"))?; - if let Some(error) = progress.error { - return Err(format!("Ollama could not download {model}: {error}")); - } - let layer = progress - .digest - .as_deref() - .and_then(|digest| digest.get(..12)) - .map(|digest| format!(" Ā· layer {digest}")) - .unwrap_or_default(); - emit_local_answer_progress( - app, - draft_id, - current, - total_steps, - format!("{}{layer}", progress.status), - progress.completed, - progress.total, - ); - } - installed_ollama_model(model)?.ok_or_else(|| { - format!("Ollama finished downloading {model}, but the model was not present afterward.") - }) -} -fn local_answer_platform_error() -> Option { - #[cfg(windows)] - { - let mut command = Command::new("cmd"); - command.args(["/C", "ver"]); - if let Ok(output) = checked_output(command, "Windows version check") - && query_setup::version_meets_minimum( - &String::from_utf8_lossy(&output.stdout), - (10, 0, 19045), - ) == Some(false) - { - return Some( - "Local grounded answers require Windows 10 22H2 or newer because that is Ollama's supported Windows baseline." - .into(), - ); - } - } - #[cfg(target_os = "macos")] - { - let mut command = Command::new("/usr/bin/sw_vers"); - command.arg("-productVersion"); - if let Ok(output) = checked_output(command, "macOS version check") - && query_setup::version_meets_minimum( - &String::from_utf8_lossy(&output.stdout), - (14, 0, 0), - ) == Some(false) - { - return Some( - "Local grounded answers require macOS 14 or newer because that is Ollama's supported macOS baseline." - .into(), - ); - } - } - None -} - -async fn prepare_local_answers_runtime( - app: &AppHandle, - state: &DesktopState, - paths: &DesktopPaths, - draft_id: &str, - current: u8, - total_steps: u8, - spec: &LocalAnswersSpec, - cancellation: background_process::CancellationToken, -) -> Result<(), String> { - if let Some(error) = local_answer_platform_error() { - return Err(error); - } - let server_ready = ollama_server_version().is_ok(); - let mut executable = resolve_query_executable(paths, &spec.managed_runtime); - if !server_ready && executable.is_none() { - let artifact = query_setup::current_artifact(&spec.managed_runtime).ok_or_else(|| { - "VidXP does not publish a managed headless Ollama runtime for this platform. Install Ollama from https://ollama.com/download, then retry." - .to_string() - })?; - let approved = app - .dialog() - .message(format!( - "Local grounded answers require a local inference runtime and {} (approximately 3.4 GB, Apache-2.0).\n\nDownload the verified headless Ollama {} runtime ({}) into VidXP's private data? No Ollama desktop app will be installed.", - spec.model, - spec.managed_runtime.version, - human_bytes(artifact.download_size_bytes) - )) - .title("Download local answer runtime") - .kind(MessageDialogKind::Info) - .buttons(MessageDialogButtons::OkCancelCustom( - "Download".into(), - "Not now".into(), - )) - .blocking_show(); - if !approved { - return Err("Local grounded-answer setup was deferred.".into()); - } - emit_local_answer_progress( - app, - draft_id, - current, - total_steps, - format!( - "Downloading the headless Ollama {} runtime", - spec.managed_runtime.version - ), - Some(0), - Some(artifact.download_size_bytes), - ); - let download_app = app.clone(); - let download_draft = draft_id.to_owned(); - let private_data = paths.private_data.clone(); - let managed_runtime = spec.managed_runtime.clone(); - let runtime_version = managed_runtime.version.clone(); - let runtime_cancellation = cancellation.clone(); - executable = Some( - tauri::async_runtime::spawn_blocking(move || { - query_setup::install_managed_runtime( - &private_data, - &managed_runtime, - &runtime_cancellation, - |downloaded, total| { - emit_local_answer_progress( - &download_app, - &download_draft, - current, - total_steps, - format!("Downloading the headless Ollama {runtime_version} runtime"), - Some(downloaded), - Some(total), - ); - }, - ) - }) - .await - .map_err(|error| { - format!("Managed Ollama runtime preparation stopped unexpectedly: {error}") - })??, - ); - } - let model_directory = paths.models.join("ollama"); - if let Some(executable) = executable { - ensure_query_service(state, &executable, &model_directory)?; - } else if !server_ready { - return Err( - "The managed Ollama runtime finished downloading, but VidXP could not locate its executable." - .to_string(), - ); - } - let pull_app = app.clone(); - let pull_draft = draft_id.to_owned(); - let pull_model = spec.model.clone(); - let pull_cancellation = cancellation; - let installed = tauri::async_runtime::spawn_blocking(move || { - pull_ollama_model( - &pull_app, - &pull_draft, - current, - total_steps, - &pull_model, - pull_cancellation, - ) - }) - .await - .map_err(|error| format!("Local answer model preparation stopped unexpectedly: {error}"))??; - if installed.digest.trim().is_empty() { - return Err(format!( - "Ollama did not report a digest for {}.", - spec.model - )); - } - Ok(()) -} fn active_local_answers(paths: &DesktopPaths) -> bool { active_runtime(paths).is_ok_and(|active| active.local_answers) @@ -1990,13 +1736,17 @@ fn configure_local_answer_environment(command: &mut Command, paths: &DesktopPath if active_local_answers(paths) { let model = manifest() .map(|manifest| manifest.local_answers.model) - .unwrap_or_else(|_| "qwen3.5:4b-q4_K_M".into()); + .unwrap_or_default(); command .env( "VIDXP_SLM_BASE_URL", format!("http://{}/v1", query_setup::OLLAMA_HOST), ) .env("VIDXP_SLM_MODEL", model); + } else { + command + .env("VIDXP_SLM_BASE_URL", "") + .env("VIDXP_SLM_MODEL", ""); } } @@ -2012,10 +1762,23 @@ fn ensure_active_query_service(state: &DesktopState, paths: &DesktopPaths) -> Re })?; ensure_query_service(state, &executable, &paths.models.join("ollama"))?; } + let active = active_runtime(paths)?; + let runtime = runtime_directory(paths, &active); let model = manifest()?.local_answers.model; - installed_ollama_model(&model)?.ok_or_else(|| { - format!("The local answer model {model} is missing. Open Setup options and repair VidXP.") - })?; + run_vidxp( + &runtime, + paths, + &[ + "local-answers".into(), + "status".into(), + "--json".into(), + "--base-url".into(), + format!("http://{}/v1", query_setup::OLLAMA_HOST), + "--model".into(), + model, + ], + "Local grounded-answer validation", + )?; Ok(()) } @@ -2648,6 +2411,38 @@ fn watch_managed_model_progress( } } +fn watch_local_answer_progress( + app: &AppHandle, + draft_id: &str, + progress_path: &Path, + current: u8, + total: u8, + stop: &AtomicBool, +) { + let mut last_contents = None; + loop { + if let Ok(contents) = fs::read(progress_path) + && last_contents.as_deref() != Some(contents.as_slice()) + && let Ok(progress) = serde_json::from_slice::(&contents) + { + emit_local_answer_progress( + app, + draft_id, + current, + total, + progress.message, + progress.current, + progress.total, + ); + last_contents = Some(contents); + } + if stop.load(Ordering::Acquire) { + break; + } + thread::sleep(Duration::from_millis(100)); + } +} + async fn uv_output( app: &AppHandle, paths: &DesktopPaths, @@ -3473,32 +3268,10 @@ async fn install_runtime( let progress_total = (if request.prepare_models { 8 } else { 7 }) + local_answer_offset; let install_result = async { - if request.local_answers { - emit_local_answer_progress( - &app, - &request.draft_id, - 2, - progress_total, - "Checking Ollama and the approved Qwen model", - None, - None, - ); - prepare_local_answers_runtime( - &app, - &state, - &paths, - &request.draft_id, - 2, - progress_total, - &manifest.local_answers, - cancellation.token(), - ) - .await?; - } emit_managed_setup_progress( &app, &request.draft_id, - 2 + local_answer_offset, + 2, progress_total, "python", "Preparing an isolated Python runtime", @@ -3533,7 +3306,7 @@ async fn install_runtime( emit_managed_setup_progress( &app, &request.draft_id, - 3 + local_answer_offset, + 3, progress_total, "package", "Acquiring the VidXP package", @@ -3564,7 +3337,7 @@ async fn install_runtime( emit_managed_setup_progress( &app, &request.draft_id, - 4 + local_answer_offset, + 4, progress_total, "dependencies", "Installing the selected search features", @@ -3591,6 +3364,66 @@ async fn install_runtime( Duration::from_secs(30), ) .await?; + + emit_local_answer_progress( + &app, + &request.draft_id, + 5, + progress_total, + "Checking Ollama and the approved Qwen model", + None, + None, + ); + let progress_path = runtime.join(".managed-local-answer-progress.json"); + let arguments = vec![ + "local-answers".into(), + "prepare".into(), + "--json".into(), + "--yes".into(), + "--no-save".into(), + "--runtime-root".into(), + paths.private_data.to_string_lossy().into_owned(), + "--base-url".into(), + format!("http://{}/v1", query_setup::OLLAMA_HOST), + "--model".into(), + manifest.local_answers.model.clone(), + "--progress-file".into(), + progress_path.to_string_lossy().into_owned(), + ]; + let preparation_app = app.clone(); + let preparation_draft_id = request.draft_id.clone(); + let monitor_stop = Arc::new(AtomicBool::new(false)); + let monitor_stop_worker = monitor_stop.clone(); + let progress_path_worker = progress_path.clone(); + let progress_monitor = thread::spawn(move || { + watch_local_answer_progress( + &preparation_app, + &preparation_draft_id, + &progress_path_worker, + 5, + progress_total, + &monitor_stop_worker, + ); + }); + let preparation = run_vidxp_supervised( + &runtime, + &paths, + &arguments, + cancellation.token(), + "Local grounded-answer preparation", + ) + .await; + monitor_stop.store(true, Ordering::Release); + let monitor_result = progress_monitor.join(); + let _ = fs::remove_file(&progress_path); + if let Err(error) = + query_setup::cleanup_managed_installation_staging(&paths.private_data) + { + log::warn!("Could not remove local-answer setup staging files: {error}"); + } + preparation?; + monitor_result + .map_err(|_| "Local-answer progress stopped unexpectedly".to_owned())?; } if let Err(error) = fs::remove_file(&runtime_wheel) { log::warn!( @@ -6146,13 +5979,12 @@ mod tests { assert_eq!(sound.extra, "sound"); assert_eq!(sound.modality, "sound"); assert_eq!(sound.label, "Sound event search"); - assert_eq!( + assert!(!sound.models.is_empty()); + assert!( sound .models .iter() - .map(|model| model.download_size_bytes) - .sum::(), - 981_760_363 + .all(|model| !model.cache_key.is_empty() && model.download_size_bytes > 0) ); assert_eq!( package_specification(&manifest, &["sound".into()], &[]), @@ -6183,9 +6015,7 @@ mod tests { .artifacts .contains_key("macos-aarch64") ); - if let Some(artifact) = - super::query_setup::current_artifact(&manifest.local_answers.managed_runtime) - { + for artifact in manifest.local_answers.managed_runtime.artifacts.values() { assert_eq!(artifact.sha256.len(), 64); assert!(artifact.download_size_bytes > 0); assert!( diff --git a/desktop/src-tauri/src/premiere_integration.rs b/desktop/src-tauri/src/premiere_integration.rs index da3d6862..10c51f44 100644 --- a/desktop/src-tauri/src/premiere_integration.rs +++ b/desktop/src-tauri/src/premiere_integration.rs @@ -2,6 +2,7 @@ use std::{ collections::BTreeSet, path::{Path, PathBuf}, process::Command, + time::Duration, }; #[cfg(windows)] @@ -11,10 +12,16 @@ use std::fs; use serde::{Deserialize, Serialize}; +use crate::background_process::{self, BackgroundPolicy}; + const CEP_ID: &str = "org.grayhat.vidxp-premiere.cep.search"; const UXP_ID: &str = "org.grayhat.vidxp-premiere"; const CEP_PACKAGE: &str = "vidxp-premiere-cep.zxp"; const UXP_PACKAGE: &str = "vidxp-premiere-uxp.ccx"; +const COMMAND_OUTPUT_LIMIT_BYTES: usize = 1024 * 1024; +#[cfg(windows)] +const DISCOVERY_TIMEOUT: Duration = Duration::from_secs(15); +const INSTALLER_TIMEOUT: Duration = Duration::from_secs(5 * 60); #[derive(Clone, Debug, Eq, Ord, PartialEq, PartialOrd, Serialize)] #[serde(rename_all = "lowercase")] @@ -215,10 +222,16 @@ foreach ($root in $roots) { } $items | ConvertTo-Json -Compress "#; - let output = Command::new("powershell.exe") - .args(["-NoProfile", "-NonInteractive", "-Command", script]) - .output(); - let Ok(output) = output else { + let mut command = Command::new("powershell.exe"); + command.args(["-NoProfile", "-NonInteractive", "-Command", script]); + let Ok(output) = background_process::run( + command, + BackgroundPolicy { + timeout: DISCOVERY_TIMEOUT, + max_output_bytes: COMMAND_OUTPUT_LIMIT_BYTES, + }, + None, + ) else { return Vec::new(); }; if !output.status.success() { @@ -338,10 +351,22 @@ fn checked_installer<'a>( installer: &Path, arguments: impl IntoIterator, ) -> Result { - let output = Command::new(installer) - .args(arguments) - .output() - .map_err(|error| format!("Could not start Adobe's plugin installer: {error}"))?; + let mut command = Command::new(installer); + command.args(arguments); + let output = background_process::run( + command, + BackgroundPolicy { + timeout: INSTALLER_TIMEOUT, + max_output_bytes: COMMAND_OUTPUT_LIMIT_BYTES, + }, + None, + ) + .map_err(|error| { + format!( + "Could not run Adobe's plugin installer: {}", + error.detail + ) + })?; let stdout = String::from_utf8_lossy(&output.stdout).trim().to_string(); let stderr = String::from_utf8_lossy(&output.stderr).trim().to_string(); if output.status.success() { diff --git a/desktop/src-tauri/src/query_setup.rs b/desktop/src-tauri/src/query_setup.rs index ad9260ea..737fd5b8 100644 --- a/desktop/src-tauri/src/query_setup.rs +++ b/desktop/src-tauri/src/query_setup.rs @@ -1,22 +1,43 @@ use std::{ collections::BTreeMap, env, fs, - fs::File, - io::{self, BufReader, Read, Write}, path::{Path, PathBuf}, - time::{Duration, SystemTime, UNIX_EPOCH}, }; -use flate2::read::GzDecoder; use serde::{Deserialize, Serialize}; -use sha2::{Digest, Sha256}; - -use crate::background_process::CancellationToken; pub(crate) const OLLAMA_HOST: &str = "127.0.0.1:11434"; const MANAGED_RUNTIME_DIRECTORY: &str = "query-runtimes"; -const RUNTIME_DOWNLOAD_TIMEOUT: Duration = Duration::from_secs(2 * 60 * 60); -const DOWNLOAD_BUFFER_BYTES: usize = 1024 * 1024; + +pub(crate) fn cleanup_managed_installation_staging(private_data: &Path) -> Result<(), String> { + let root = private_data.join(MANAGED_RUNTIME_DIRECTORY); + if !root.exists() { + return Ok(()); + } + let entries = fs::read_dir(&root) + .map_err(|error| format!("Could not inspect {}: {error}", root.display()))?; + for entry in entries { + let entry = entry.map_err(|error| { + format!("Could not inspect an entry under {}: {error}", root.display()) + })?; + let path = entry.path(); + let name = entry.file_name(); + let name = name.to_string_lossy(); + if !name.starts_with(".ollama-") { + continue; + } + if name.ends_with(".download") && path.is_file() { + fs::remove_file(&path).map_err(|error| { + format!("Could not remove {}: {error}", path.display()) + })?; + } else if name.ends_with(".partial") && path.is_dir() { + fs::remove_dir_all(&path).map_err(|error| { + format!("Could not remove {}: {error}", path.display()) + })?; + } + } + Ok(()) +} #[derive(Clone, Debug, Deserialize, Serialize)] pub(crate) struct ManagedRuntimeSpec { @@ -41,23 +62,6 @@ pub(crate) enum ManagedRuntimeArchive { TarGz, } -pub(crate) fn version_meets_minimum(output: &str, minimum: (u32, u32, u32)) -> Option { - let version = output - .split(|character: char| !(character.is_ascii_digit() || character == '.')) - .find(|candidate| candidate.contains('.'))?; - let parts = version - .split('.') - .take(3) - .map(str::parse::) - .collect::, _>>() - .ok()?; - if parts.len() < 2 { - return None; - } - let actual = (parts[0], parts[1], parts.get(2).copied().unwrap_or(0)); - Some(actual >= minimum) -} - pub(crate) fn executable_candidates() -> Vec { let mut candidates = Vec::new(); if cfg!(windows) { @@ -82,36 +86,29 @@ pub(crate) fn executable_candidates() -> Vec { candidates } -#[cfg(windows)] -fn find_winget_ollama_in(root: &Path) -> Option { - let entries = fs::read_dir(root).ok()?; - let mut matches = Vec::new(); - for entry in entries.flatten() { - let package = entry.path(); - if !package.is_dir() - || !package - .file_name() - .is_some_and(|name| name.to_string_lossy().starts_with("Ollama.Ollama_")) - { - continue; - } - let executable = package.join("ollama.exe"); - if executable.is_file() { - matches.push(executable); - } - } - matches.sort(); - matches.into_iter().next() -} - #[cfg(windows)] pub(crate) fn resolve_winget_ollama_executable() -> Option { - let local = env::var_os("LOCALAPPDATA")?; - let root = PathBuf::from(local) + let root = PathBuf::from(env::var_os("LOCALAPPDATA")?) .join("Microsoft") .join("WinGet") .join("Packages"); - find_winget_ollama_in(&root) + let mut matches = fs::read_dir(root) + .ok()? + .flatten() + .map(|entry| entry.path()) + .filter(|package| { + package.is_dir() + && package.file_name().is_some_and(|name| { + name.to_string_lossy().starts_with("Ollama.Ollama_") + }) + && package.join("ollama.exe").is_file() + }) + .map(|package| package.join("ollama.exe")) + .collect::>(); + matches.sort(); + matches + .into_iter() + .next() .and_then(|candidate| fs::canonicalize(&candidate).ok().or(Some(candidate))) } @@ -133,390 +130,16 @@ fn current_platform_key() -> Option<&'static str> { None } -pub(crate) fn current_artifact(spec: &ManagedRuntimeSpec) -> Option<&ManagedRuntimeArtifactSpec> { - spec.artifacts.get(current_platform_key()?) -} - -fn managed_runtime_root(private_data: &Path) -> PathBuf { - private_data.join(MANAGED_RUNTIME_DIRECTORY) -} - -fn managed_runtime_directory( - private_data: &Path, - spec: &ManagedRuntimeSpec, -) -> Result { - if spec.version.is_empty() - || !spec.version.chars().all(|character| { - character.is_ascii_alphanumeric() || matches!(character, '.' | '-' | '_') - }) - { - return Err("The managed Ollama runtime version is invalid.".into()); - } - Ok(managed_runtime_root(private_data).join(format!("ollama-{}", spec.version))) -} - pub(crate) fn managed_executable( private_data: &Path, spec: &ManagedRuntimeSpec, ) -> Option { - let artifact = current_artifact(spec)?; - let candidate = managed_runtime_directory(private_data, spec) - .ok()? + let artifact = spec.artifacts.get(current_platform_key()?)?; + let candidate = private_data + .join(MANAGED_RUNTIME_DIRECTORY) + .join(format!("ollama-{}", spec.version)) .join(&artifact.executable); candidate .is_file() .then(|| fs::canonicalize(&candidate).unwrap_or(candidate)) } - -fn runtime_download_client() -> Result { - reqwest::blocking::Client::builder() - .connect_timeout(Duration::from_secs(15)) - .timeout(RUNTIME_DOWNLOAD_TIMEOUT) - .build() - .map_err(|error| format!("Could not configure the Ollama runtime download: {error}")) -} - -fn download_archive( - artifact: &ManagedRuntimeArtifactSpec, - destination: &Path, - cancellation: &CancellationToken, - mut progress: impl FnMut(u64, u64), -) -> Result<(), String> { - let mut response = runtime_download_client()? - .get(&artifact.url) - .header(reqwest::header::USER_AGENT, "VidXP-Desktop") - .send() - .and_then(reqwest::blocking::Response::error_for_status) - .map_err(|error| format!("Could not download the managed Ollama runtime: {error}"))?; - let response_total = response - .content_length() - .unwrap_or(artifact.download_size_bytes); - let mut output = File::create(destination).map_err(|error| { - format!( - "Could not create the temporary Ollama runtime archive at {}: {error}", - destination.display() - ) - })?; - let mut hasher = Sha256::new(); - let mut buffer = vec![0_u8; DOWNLOAD_BUFFER_BYTES]; - let mut downloaded = 0_u64; - progress(0, response_total); - loop { - if cancellation.is_cancelled() { - return Err("the managed Ollama runtime download was cancelled".into()); - } - let count = response - .read(&mut buffer) - .map_err(|error| format!("The managed Ollama runtime download failed: {error}"))?; - if count == 0 { - break; - } - output.write_all(&buffer[..count]).map_err(|error| { - format!("Could not write the managed Ollama runtime archive: {error}") - })?; - hasher.update(&buffer[..count]); - downloaded += count as u64; - progress(downloaded, response_total); - } - output - .sync_all() - .map_err(|error| format!("Could not finish the managed Ollama runtime archive: {error}"))?; - if downloaded != artifact.download_size_bytes { - return Err(format!( - "The managed Ollama runtime download contained {downloaded} bytes; expected {}.", - artifact.download_size_bytes - )); - } - let actual_sha256 = hex::encode(hasher.finalize()); - if !actual_sha256.eq_ignore_ascii_case(&artifact.sha256) { - return Err(format!( - "The managed Ollama runtime failed checksum verification: expected {}, received {actual_sha256}.", - artifact.sha256 - )); - } - Ok(()) -} - -fn extract_zip( - archive_path: &Path, - destination: &Path, - cancellation: &CancellationToken, -) -> Result<(), String> { - let archive_file = File::open(archive_path) - .map_err(|error| format!("Could not open the managed Ollama archive: {error}"))?; - let mut archive = zip::ZipArchive::new(BufReader::new(archive_file)) - .map_err(|error| format!("Could not read the managed Ollama ZIP archive: {error}"))?; - for index in 0..archive.len() { - if cancellation.is_cancelled() { - return Err("the managed Ollama runtime extraction was cancelled".into()); - } - let mut entry = archive - .by_index(index) - .map_err(|error| format!("Could not inspect the managed Ollama archive: {error}"))?; - let relative = entry - .enclosed_name() - .ok_or("The managed Ollama archive contains an unsafe path.")?; - if entry - .unix_mode() - .is_some_and(|mode| mode & 0o170000 == 0o120000) - { - return Err("The managed Ollama ZIP archive contains an unsupported link.".into()); - } - let output = destination.join(relative); - if entry.is_dir() { - fs::create_dir_all(&output) - .map_err(|error| format!("Could not create an Ollama runtime folder: {error}"))?; - } else if entry.is_file() { - if let Some(parent) = output.parent() { - fs::create_dir_all(parent).map_err(|error| { - format!("Could not create an Ollama runtime folder: {error}") - })?; - } - let mut file = File::create(&output) - .map_err(|error| format!("Could not extract an Ollama runtime file: {error}"))?; - io::copy(&mut entry, &mut file) - .map_err(|error| format!("Could not extract an Ollama runtime file: {error}"))?; - } else { - return Err("The managed Ollama ZIP archive contains an unsupported entry.".into()); - } - } - Ok(()) -} - -fn extract_tar_gz( - archive_path: &Path, - destination: &Path, - cancellation: &CancellationToken, -) -> Result<(), String> { - let archive_file = File::open(archive_path) - .map_err(|error| format!("Could not open the managed Ollama archive: {error}"))?; - let decoder = GzDecoder::new(BufReader::new(archive_file)); - let mut archive = tar::Archive::new(decoder); - let entries = archive - .entries() - .map_err(|error| format!("Could not read the managed Ollama archive: {error}"))?; - for entry in entries { - if cancellation.is_cancelled() { - return Err("the managed Ollama runtime extraction was cancelled".into()); - } - let mut entry = entry - .map_err(|error| format!("Could not inspect the managed Ollama archive: {error}"))?; - let entry_type = entry.header().entry_type(); - if !entry_type.is_file() && !entry_type.is_dir() { - return Err("The managed Ollama archive contains an unsupported entry.".into()); - } - if !entry - .unpack_in(destination) - .map_err(|error| format!("Could not extract the managed Ollama archive: {error}"))? - { - return Err("The managed Ollama archive contains an unsafe path.".into()); - } - } - Ok(()) -} - -fn extract_archive( - archive_path: &Path, - destination: &Path, - archive: ManagedRuntimeArchive, - cancellation: &CancellationToken, -) -> Result<(), String> { - fs::create_dir_all(destination).map_err(|error| { - format!( - "Could not create the managed Ollama runtime folder at {}: {error}", - destination.display() - ) - })?; - match archive { - ManagedRuntimeArchive::Zip => extract_zip(archive_path, destination, cancellation), - ManagedRuntimeArchive::TarGz => extract_tar_gz(archive_path, destination, cancellation), - } -} - -pub(crate) fn install_managed_runtime( - private_data: &Path, - spec: &ManagedRuntimeSpec, - cancellation: &CancellationToken, - progress: impl FnMut(u64, u64), -) -> Result { - if let Some(executable) = managed_executable(private_data, spec) { - return Ok(executable); - } - let artifact = current_artifact(spec).ok_or_else(|| { - "VidXP does not publish a managed Ollama runtime for this operating system and architecture." - .to_string() - })?; - let runtime_root = managed_runtime_root(private_data); - fs::create_dir_all(&runtime_root).map_err(|error| { - format!( - "Could not create the managed query runtime folder at {}: {error}", - runtime_root.display() - ) - })?; - let target = managed_runtime_directory(private_data, spec)?; - let nonce = SystemTime::now() - .duration_since(UNIX_EPOCH) - .map_err(|error| format!("The system clock is invalid: {error}"))? - .as_nanos(); - let temporary_name = format!("ollama-{}-{}-{nonce}", spec.version, std::process::id()); - let archive_path = runtime_root.join(format!(".{temporary_name}.download")); - let staging = runtime_root.join(format!(".{temporary_name}.partial")); - let result = (|| { - download_archive(artifact, &archive_path, cancellation, progress)?; - if cancellation.is_cancelled() { - return Err("the managed Ollama runtime setup was cancelled".into()); - } - extract_archive(&archive_path, &staging, artifact.archive, cancellation)?; - let staged_executable = staging.join(&artifact.executable); - if !staged_executable.is_file() { - return Err(format!( - "The managed Ollama archive did not contain {}.", - artifact.executable.display() - )); - } - if target.exists() { - fs::remove_dir_all(&target).map_err(|error| { - format!("Could not replace the incomplete managed Ollama runtime: {error}") - })?; - } - fs::rename(&staging, &target) - .map_err(|error| format!("Could not activate the managed Ollama runtime: {error}"))?; - let executable = target.join(&artifact.executable); - Ok(fs::canonicalize(&executable).unwrap_or(executable)) - })(); - let _ = fs::remove_file(&archive_path); - if staging.exists() { - let _ = fs::remove_dir_all(&staging); - } - result -} - -#[cfg(test)] -mod tests { - use super::*; - - fn temporary_root(label: &str) -> PathBuf { - std::env::temp_dir().join(format!( - "vidxp-{label}-{}-{}", - std::process::id(), - SystemTime::now() - .duration_since(UNIX_EPOCH) - .expect("clock") - .as_nanos() - )) - } - - fn runtime_spec() -> ManagedRuntimeSpec { - ManagedRuntimeSpec { - version: "0.32.5".into(), - maximum_download_size_bytes: 10, - artifacts: BTreeMap::from([( - current_platform_key().unwrap_or("unsupported").into(), - ManagedRuntimeArtifactSpec { - url: "https://example.invalid/ollama.zip".into(), - sha256: "00".repeat(32), - download_size_bytes: 10, - archive: ManagedRuntimeArchive::Zip, - executable: PathBuf::from(if cfg!(windows) { - "ollama.exe" - } else { - "ollama" - }), - }, - )]), - } - } - - #[test] - fn platform_versions_are_compared_as_numeric_triples() { - assert_eq!( - version_meets_minimum("Microsoft Windows [Version 10.0.19045.1]", (10, 0, 19045)), - Some(true) - ); - assert_eq!( - version_meets_minimum("Microsoft Windows [Version 10.0.19044.1]", (10, 0, 19045)), - Some(false) - ); - assert_eq!(version_meets_minimum("14.0.0", (14, 0, 0)), Some(true)); - assert_eq!(version_meets_minimum("14.0", (14, 0, 0)), Some(true)); - assert_eq!(version_meets_minimum("13.6.9", (14, 0, 0)), Some(false)); - assert_eq!(version_meets_minimum("unknown", (14, 0, 0)), None); - } - - #[test] - fn managed_runtime_version_cannot_escape_its_owned_root() { - let mut spec = runtime_spec(); - spec.version = "../escape".into(); - assert!(managed_runtime_directory(Path::new("runtime-root"), &spec).is_err()); - } - - #[test] - fn managed_executable_requires_the_expected_file() { - let root = temporary_root("managed-ollama-path"); - let spec = runtime_spec(); - assert_eq!(managed_executable(&root, &spec), None); - fs::remove_dir_all(root).ok(); - } - - #[test] - fn zip_runtime_archive_extracts_only_expected_files() { - let root = temporary_root("managed-ollama-zip"); - let archive_path = root.join("runtime.zip"); - let destination = root.join("extracted"); - fs::create_dir_all(&root).expect("temporary root"); - let archive_file = File::create(&archive_path).expect("archive file"); - let mut archive = zip::ZipWriter::new(archive_file); - archive - .start_file( - "ollama.exe", - zip::write::SimpleFileOptions::default() - .compression_method(zip::CompressionMethod::Deflated), - ) - .expect("archive entry"); - archive.write_all(b"headless-runtime").expect("entry data"); - archive.finish().expect("finished archive"); - - extract_zip(&archive_path, &destination, &CancellationToken::default()) - .expect("extracted archive"); - - assert_eq!( - fs::read(destination.join("ollama.exe")).expect("extracted executable"), - b"headless-runtime" - ); - fs::remove_dir_all(root).expect("temporary cleanup"); - } - - #[test] - fn tar_runtime_archive_extracts_only_expected_files() { - let root = temporary_root("managed-ollama-tar"); - let archive_path = root.join("runtime.tgz"); - let destination = root.join("extracted"); - fs::create_dir_all(&root).expect("temporary root"); - let archive_file = File::create(&archive_path).expect("archive file"); - let encoder = flate2::write::GzEncoder::new(archive_file, flate2::Compression::default()); - let mut archive = tar::Builder::new(encoder); - let contents = b"headless-runtime"; - let mut header = tar::Header::new_gnu(); - header.set_size(contents.len() as u64); - header.set_mode(0o755); - header.set_cksum(); - archive - .append_data(&mut header, "ollama", &contents[..]) - .expect("archive entry"); - archive - .into_inner() - .expect("archive encoder") - .finish() - .expect("finished archive"); - fs::create_dir_all(&destination).expect("extraction destination"); - - extract_tar_gz(&archive_path, &destination, &CancellationToken::default()) - .expect("extracted archive"); - - assert_eq!( - fs::read(destination.join("ollama")).expect("extracted executable"), - contents - ); - fs::remove_dir_all(root).expect("temporary cleanup"); - } -} diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md index 1fcb66dc..ed6c3b25 100644 --- a/docs/CONTRIBUTING.md +++ b/docs/CONTRIBUTING.md @@ -16,7 +16,14 @@ documentation, tests, or product changes. - Explain whether a storage or model change requires existing repositories to be rebuilt. - Do not commit generated environments, model weights, media, indexes, local - data, benchmark runs, or build outputs. + databases, bulky raw benchmark output, machine-specific data, or build + outputs. +- A focused pull request may include a lightweight, sanitized benchmark result + when it directly supports the change or a documented research claim. Store + it with the benchmark documentation, link it from the owning result or metric + page, and retain only the configuration, provenance, metrics, and traces a + reviewer needs to audit it from another checkout. Maintainers make the final + retention decision during review. GPU support is deferred. Do not make CUDA the default or publish a GPU installation path without a separately reviewed implementation and validation diff --git a/docs/adding-a-capability.md b/docs/adding-a-capability.md index 55509e33..24fb58ec 100644 --- a/docs/adding-a-capability.md +++ b/docs/adding-a-capability.md @@ -139,16 +139,13 @@ meaning may make existing indexes invalid. When it does, require a rebuild and keep the previous index active until the replacement passes validation. State the rebuild requirement in both the release note and user documentation. -Regenerate the checked-in Desktop catalogs after changing a capability label, -description, package extra, or model contract: +Desktop development and package commands generate ignored catalog files from +these specifications. Before invoking Cargo directly, generate them with: ```bash -npm --prefix desktop run model-catalog:write -npm --prefix desktop run model-catalog:check +npm --prefix desktop run model-catalog:generate ``` -Review the generated diff rather than editing the catalog by hand. - ## 6. Register the capability After the definition and executor are ready, connect them with a diff --git a/docs/architecture/platform.md b/docs/architecture/platform.md index a0b03b29..50b0b12c 100644 --- a/docs/architecture/platform.md +++ b/docs/architecture/platform.md @@ -503,6 +503,9 @@ by that immutable snapshot. Indexing and search therefore coexist safely. The Chroma adapter stores generation identity with every record and implements snapshot-scoped search and garbage collection. Chroma remains replaceable behind the `IndexRepository` port; snapshot semantics do not depend on Chroma collection layout. +Index schema 8 defaults collections to inner-product distance so PE-A retains its +released dot-product frame ordering. Scene, action, and default speech vectors are +unit-normalized, so their ordering is unchanged from squared L2 distance. For the embedded adapter, `indexes/store/` is the shared physical Chroma database; generation directories own manifests and checkpoints, while exact generation record counts in those manifests are revalidated before committed reads. A missing database, @@ -773,18 +776,17 @@ composition root and is sorted deterministically. `97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3`; its published multilingual MTEB retrieval results materially exceed the older multilingual E5 baseline and its Apache-2.0 license permits the intended deployment. -- Sound: use FineLAP at immutable Hugging Face revision - `b419aa22947d29907a5567f21b81bf3b39a40449`. Each video audio stream is decoded - once into ten-second windows. The sound collection stores one normalized global - embedding per window and the model's normalized dense embeddings as timestamped - activation records. Both use the shared text/audio space, and search results - retain `representation`, window, and activation provenance. FineLAP requires - repository-supplied Transformers code; VidXP loads only the pinned snapshot, - keeps runtime loading offline, and prepares the two small pinned RoBERTa - tokenizer artifacts explicitly instead of allowing a constructor-time model - download. The Hugging Face model card declares MIT; the upstream GitHub source - repository does not contain a separate license file, so redistribution review - must preserve that qualification. +- Sound: use PE-A-Frame Small at immutable Hugging Face revision + `e5fc71c1f0be50279f52f292390b589780079e13`. Its released audio and text heads + produce one comparable embedding every 40 ms; the sound collection ranks those + embeddings with inner product, matching the checkpoint's scoring rule. VidXP + decodes at 48 kHz and defaults to ten-second inference sections with two seconds + of overlap. Each overlap is split at its midpoint so a global timestamp is stored + once. Search keeps the best frame score per fixed ten-second evidence window; + the exact frame timestamp remains in metadata. The section length, overlap, + evidence window, and batch size of one are configurable VidXP deployment + defaults, not methods claimed from the PE-A paper. FineLAP remains benchmark-only + to reproduce the recorded provider comparison. - Actor: replace `face_recognition`/dlib with OpenCV Zoo YuNet plus SFace through OpenCV's maintained DNN APIs. Model files are retrieved with `pooch`, pinned to OpenCV Zoo commit `47534e27c9851bb1128ccc0102f1145e27f23f98`, and verified @@ -838,7 +840,9 @@ Flow: 2. Ask an injected local SLM planner for a strictly typed `QueryPlan`. 3. Validate that the plan uses registered operations and safe parameters only. 4. Execute retrieval through application search/capability operations. -5. Fuse overlapping intervals with deterministic reciprocal-rank fusion. +5. Build bounded candidates from rank-anchored, directly overlapping evidence, + then order them with deterministic reciprocal-rank fusion. Indirect overlap + cannot join separate moments. 6. Ask the answer synthesizer for a grounded answer. 7. Return `QueryAnswer` with timestamped citations and supporting hits. @@ -867,25 +871,43 @@ reproducibility. The default local query model is the official Ollama `qwen3.5:4b-q4_K_M` artifact: Qwen3.5 4B with Q4_K_M quantization. Enabling a self-hosted Ollama base URL selects that model unless an operator explicitly -overrides it. VidXP sets temperature zero, disables reasoning output, and -requires the native JSON schemas for both planning and synthesis. Model weights -are never bundled. Desktop setup pulls the approved artifact only after the -user selects local grounded answers and approves any required headless-runtime -download; CLI and server operators pull it explicitly. - -Desktop treats the provider as an optional supervised runtime. It first probes -the loopback `/api/version` and `/api/tags` contracts and reuses an existing -healthy service without taking ownership. It next reuses an existing Ollama -executable. If neither is available on a supported Desktop target, it downloads -the pinned official headless archive declared in the embedded runtime manifest, -verifies its expected byte count and SHA-256 digest, and atomically activates it -under Desktop's private application data. Desktop never installs the Ollama -desktop app. It starts a child `ollama serve` process that its existing -process-tree supervisor owns, and the model pull uses Ollama's streaming -`/api/pull` contract. Desktop persists only the feature selection, injects the -private `/v1` endpoint and approved model into managed processes, and includes -the same non-secret environment in stdio MCP configuration. It never stops an -externally owned Ollama service. +overrides it. VidXP disables reasoning output and requires native JSON schemas +for both planning and synthesis. For non-thinking requests it follows the +[Qwen3.5-4B model guidance](https://huggingface.co/Qwen/Qwen3.5-4B): a 32,768 +token response ceiling, temperature `0.7`, top-p `0.8`, and presence penalty +`1.5`. The ceiling prevents an unsupported truncation policy; it is not a +target response length, and generation normally stops earlier. The pinned +Ollama OpenAI-compatible interface does not expose Qwen's recommended top-k, +min-p, or repetition-penalty controls through this adapter, so VidXP does not +pretend to apply them. + +Managed runtimes start with 64,000 context tokens, the minimum recommended by +[Ollama for agents and tool use](https://docs.ollama.com/context-length). +They enable Flash Attention and an 8-bit KV cache and run one request at a time; +Ollama documents that combination as reducing context memory with little +quality loss. These are runtime recommendations, not results from a VidXP +retrieval paper. Operators can override the context and output ceilings with +`VIDXP_SLM_CONTEXT_TOKENS` and `VIDXP_SLM_MAX_OUTPUT_TOKENS`. An externally +owned Ollama service must be configured at service start because VidXP never +restarts it. + +Model weights are never bundled. Desktop setup pulls the approved artifact only +after the user selects local grounded answers and approves any required +headless-runtime download. CLI and server operators use +`vidxp local-answers prepare`. + +The Python local-answer service owns endpoint checks, runtime discovery and +installation, checksum verification, model download, and saved CLI +configuration. Its approved runtime and model are declared once in +`src/vidxp/assets/local-answers.json`. CLI setup calls that service directly; +Desktop invokes the same CLI operation inside its managed VidXP runtime. + +Desktop still owns process supervision. It starts `ollama serve` only when the +shared setup selected a managed executable, injects the private `/v1` endpoint +and approved model into managed processes, and includes the same non-secret +environment in stdio MCP configuration. It never stops an externally owned +Ollama service. A local CLI installation can start its saved managed runtime on +demand and stops only the process it started. Published model results select the integration candidate; the repository gate does not attempt to reproduce general model leaderboards. Promotion still diff --git a/docs/benchmarking/README.md b/docs/benchmarking/README.md index 4e9920fa..98ad81ac 100644 --- a/docs/benchmarking/README.md +++ b/docs/benchmarking/README.md @@ -15,10 +15,12 @@ installation and product usage, start with the main | Shared benchmark support | Complete | Stable IDs, time ranges, metadata, top-k retrieval, isolated runs, checkpoints, and prediction files are implemented | | Guided input preparation | Complete | `vidxp benchmark prepare` estimates and confirms downloads, verifies pinned artifacts, validates DiDeMo media, resumes partial transfers, and prints the runnable benchmark command | | DiDeMo visual localization | Legacy full result + current smoke | The legacy CLIP stack completed 4,021 official test queries over 1,037 videos; the current SigLIP2 stack passed a one-annotation real execution smoke | +| Action/video retrieval | VideoPrism retained by a small candidate gate; canonical runs pending | VideoPrism scored 50/50 on a five-class Kinetics-mini gate. MSR-VTT 1K-A and Charades-STA remain the required corpus-ranking and temporal tests. | | HiREST transcript localization | Legacy full result + current smoke | The legacy MiniLM stack scored all 193 validation pairs; current Qwen3 passed a two-video real execution smoke; 776 released test predictions remain unscored because their public bounds are placeholders | -| Environmental-sound retrieval | Implementation complete; benchmark pending | FineLAP stores global ten-second windows and dense timestamped sound activations; no VidXP quality score is claimed yet | -| LongVALE combined evaluation | Next adapter and pilot | Validate vision, environmental sound, and speech together on one evaluation archive before scheduling the full run | -| Codex MCP ablation | Runnable scaffold; not run | Promptfoo pairs the same Codex video tasks with and without VidXP MCP; no agent result is claimed yet | +| Environmental-sound retrieval | PE-A-Frame Small integrated; long-audio gate pending | An identical 149-query AEGBench comparison selected PE-A-Frame over FineLAP. The product now indexes its 40 ms frames through bounded overlapping sections and returns distinct ten-second evidence windows. | +| LongVALE combined evaluation | Selected pilot complete | The current 81-run Codex pilot and 54-case local-SLM comparison use nine held-out tasks. This selected set is not an official LongVALE result. | +| Codex MCP ablation | Isolated pilot scored; quality gate failed | VidXP used fewer tokens and less time than direct inspection, but Success@3 was 15/27 versus 18/27. Its visible evidence reached 19/27. | +| Local SLM routing | Router and planner scored | One-request router/planner Success@3 was 15/27 and 19/27. The planner averaged 270 local tokens and 25.544 seconds with no external-provider call. | | Actor clustering | Data-gated | The preferred BBT/Buffy evaluation still requires lawful access to the source episodes | Read [current results](results.md) for the scores, plain-language metric @@ -29,10 +31,13 @@ definitions, honest comparisons, and the next benchmark decision. | If you need toā€¦ | Read | |---|---| | Understand how VidXP performed | [Current results](results.md) | +| Start a paper-facing audit of benchmark premise, constraints, consolidated metrics, machine profiles, and retained artifacts | [Metric database](metric_database.md) | +| See the required per-modality gates and exact commands | [Individual modality gates](modality_gates.md) | | Reproduce DiDeMo or HiREST | [Adapter validation ledger](adapter_validation.md) | | Understand the benchmark-ready Python structure | [Core contract](core_contract.md) | | See which benchmarks exist and what each measures | [Benchmark catalog](benchmark_catalog.md) | -| Understand the current model and benchmark choices | [Multimodal model direction](model_selection.md) | +| Understand the current product and evaluation choices | [Evidence retrieval direction](model_selection.md) | +| See exactly which paper-derived ideas are in the product | [Research adoption record](research_adoption.md) | | Run the Codex MCP-on/MCP-off experiment | [Codex agent ablation](agent_ablation.md) | | Find exact published competitor scores | [Published comparison results](published_results.md) | | Review the relevant papers | [Research-paper inventory](research_papers.md) | @@ -53,13 +58,45 @@ together. The retained full DiDeMo and HiREST results establish separate legacy-provider visual and transcript baselines. Current SigLIP2 and Qwen3 checks establish adapter/runtime compatibility only; they do not yet provide full-corpus quality -comparisons. VidXP now contributes separate visual, speech, and FineLAP sound -evidence, including global windows and dense timestamps for music, alarms, -barking, and other non-speech events. The next target is the LongVALE adapter and -one-archive pilot. That work must measure the integration before any VidXP sound -quality or combined-system claim is made. The -[current model direction](model_selection.md) records the selection evidence and -remaining controls. +comparisons. VidXP can emit visual, speech, and PE-A-Frame sound evidence. The +AEGBench adapter compared FineLAP with PE-A-Frame Small over 149 valid event +queries and selected the latter for the product. That frozen subset is a +provider decision, not a full dataset or long-audio product score. +The earlier LongVALE-derived target-only result remains provenance only. + +The corrected Codex MCP development smoke returned a useful opening clip in all +three conditions. Against direct local inspection, VidXP matched the primary +bounded-chunk result with 40.9% fewer tokens and 22.1% lower latency. Its saved +top result was `0ā€“10` seconds; the agent expanded the answer to `0ā€“12`, reducing +answer IoU from `.600` to `.500`. This is one development task, not a product +gate or held-out quality claim. Older smokes remain debugging history because +their prompts named the tool path and reused condition state. +Earlier local controls exposed a +separate historical FineLAP integration error: global clip and dense activation +records were cross-ranked. Separating those representations was correct, but +the later selector produced no target-overlapping final top-three result on the +four-task component control. A later input audit found that control cannot +decide provider quality: one reference has no audible event, and another sound +query has several valid occurrences but only one accepted interval. That result +is an auxiliary diagnosis; it neither validates nor rejects the selector and it +does not decide whether the collective agent comparison can run. + +The current isolated Codex pilot completed 81 runs with all 27 primary pairs +valid and scorable. VidXP was 18.3% faster and used 20.6% fewer tokens than +direct inspection, but Success@3 was 15/27 versus 18/27, so the paired quality +gate failed. VidXP's visible MCP evidence reached 19/27 within its top three; +the agent did not consistently return all of it. + +The separate 54-case local-SLM comparison makes one planning request and then +returns exactly three bounded VidXP windows without model reranking. The basic +router scored 15/27; allowing the planner to vary query, modalities, and +candidate depth scored 19/27 at 25.544 seconds and 270 local tokens per case. +This supports a selected-pilot local-routing result, not general accuracy or an +official LongVALE score. Success@1, rank, exact boundaries, and raw evidence-tile +recall remain visible rather than being replaced by Success@3. + +See [current model direction](model_selection.md) and the +[research adoption record](research_adoption.md). ## Evidence rules diff --git a/docs/benchmarking/agent_ablation.md b/docs/benchmarking/agent_ablation.md index 86287040..8aa9f7b7 100644 --- a/docs/benchmarking/agent_ablation.md +++ b/docs/benchmarking/agent_ablation.md @@ -1,45 +1,99 @@ -# Codex evaluation with and without VidXP MCP +# Codex evaluation with and without the VidXP integration Collection index: [Benchmarking research](README.md) -Status: Runnable scaffold; no agent results recorded +Status: Codex pilot and local-SLM comparison scored -Last verified: 2026-09-01 +Last verified: 2026-09-07 -This experiment measures whether access to VidXP through its local stdio MCP -server improves a Codex agent's ability to find timestamped evidence in long -videos. It is a product-level ablation, not a replacement for published model -benchmarks such as MAEB, MVEB, or AEGBench. +This experiment measures whether the complete VidXP agent integration improves +a Codex agent's ability to find timestamped evidence in long videos. The +integration consists of the shipped video-evidence skill and the local stdio +MCP server. It is a product-level ablation, not a replacement for published +model benchmarks such as MAEB, MVEB, or AEGBench. + +The primary product question is whether the agent returns the event within a +small ranked set of practical clips while using fewer tokens. Exact temporal +IoU and top-one ordering remain secondary measurements; neither is discarded +or presented as the entire serving objective. ## What the comparison holds constant -Every task runs once in each condition with the same Codex model, reasoning -effort, prompt, media workspace, filesystem sandbox, network policy, output -schema, and fresh thread: +Each repetition uses the same Codex model, reasoning effort, user prompt, task, +source-video identity, output schema, and fresh thread. The direct-local and +clean-user workspaces receive hard links to the same bytes. VidXP indexes those +bytes before timing, then its agent workspace omits the relative source path so +ordinary shell inspection fails. | Condition | VidXP access | Purpose | | --- | --- | --- | -| `codex-vidxp-mcp` | The local `vidxp-mcp` stdio server | Measure the complete agent-plus-VidXP workflow | -| `codex-no-mcp` | No MCP server and no direct VidXP CLI use | Measure what the same Codex agent can recover from the local media without VidXP | - -The two conditions use an isolated `CODEX_HOME` that contains authentication but -no ambient MCP servers, plugins, or skills. Promptfoo receives the MCP definition -through the Codex provider's `cli_config`; the MCP-off provider receives no such -definition. Streaming traces must prove that MCP-on used at least one VidXP tool -and MCP-off neither used a VidXP tool nor invoked the VidXP CLI through the shell. - -The committed configuration disables network access, persistent threads, result -caching, provider retries, parallel execution, and Codex subagents. These -controls reduce leakage, cross-task state, and accidental extra model runs. - -## Why Promptfoo owns orchestration +| `codex-vidxp` | The committed `vidxp-find-video-evidence` skill and local `vidxp-mcp` server | Measure the complete installed agent-plus-VidXP workflow | +| `codex-baseline` | No VidXP skill, MCP server, or direct VidXP CLI use; system commands plus the host FFmpeg and ffprobe installation are available | Measure what the same Codex agent does without VidXP | +| `codex-clean-user` | Writable terminal and network, but an initial PATH containing only operating-system commands; no VidXP skill or MCP | Measure what a non-developer setup can bootstrap without inheriting the host's Homebrew or repository tools | + +Each condition has a separate `CODEX_HOME` and working directory. Setup copies +only authentication from a common isolated login home; it does not share +configuration, sessions, or discovered skills. It installs the committed skill +only in the VidXP workspace and passes the MCP definition only to that provider. +Before every condition run, a Promptfoo hook clears prior outputs and installed +tools from that condition's workspace. It retains fixed media for the two +non-VidXP conditions and only the committed skill for VidXP. This makes +repetitions independent instead of +letting a previous agent's files or clean-user bootstrap affect the next run. +Preflight checks both hard links, rejects any VidXP-on source path, rejects +ambient MCP configuration and leaked VidXP skills, and verifies that the +clean-user login shell cannot initially resolve +`ffmpeg`, `ffprobe`, `vidxp`, or `vidxp-mcp`. +Each condition uses an +[OpenAI-documented Codex permission profile](https://developers.openai.com/codex/permissions) +that denies filesystem-root access, reopens only Codex's minimal runtime paths +and its own writable workspace, and sets network access for that condition. +The direct-local profile also reads the installation prefix containing FFmpeg +and ffprobe. The clean-user and VidXP profiles cannot execute those host +binaries, even by absolute path. On macOS, before any model call, preflight runs +the pinned Codex sandbox and verifies the denied host read, allowed workspace +read and write, and expected FFmpeg access for all three conditions. A command +that tries a blocked host path is not a breach. The scorer separately rejects +actual VidXP use in non-VidXP conditions and direct source-media inspection in +the VidXP condition. + +The scorer enforces capability boundaries, not an agent script. The direct-local +baseline cannot call VidXP but may use system commands, FFmpeg, and ffprobe. The +clean-user condition retains its terminal and network and may install tools into +its own workspace; Homebrew and the repository environment are absent from its +initial PATH. The VidXP condition +cannot inspect media directly through the agent shell, but the MCP server may +use VidXP's configured FFmpeg runtime internally. Loading the skill or following +one discovery sequence is not required; the agent must submit a matching MCP +retrieval and return evidence from its fresh durable result. The user prompt +never names VidXP, FFmpeg, a condition, or a required call sequence. Skill use, +polling choices, model turns, and Promptfoo-recorded items and tool calls remain +reported. + +The VidXP and direct-local permission profiles disable network access. The +clean-user profile enables unrestricted command-line network access so the +agent can bootstrap tools. Every lane disables persistent +threads, result caching, provider retries, parallel execution, and Codex +subagents. + +The timed comparison starts after setup: all five videos are already indexed +for scene, action, sound, and speech in VidXP. Download, model preparation, +media import, and indexing are excluded from all three agent times and measured +separately below. + +## Why Promptfoo is the execution harness [Promptfoo](https://www.promptfoo.dev/docs/providers/openai-codex-sdk/) runs the -paired provider matrix, repetitions, structured output, traces, usage -collection, and local reports. VidXP's Python benchmark code owns task expansion -and deterministic scoring. This division avoids rebuilding a general evaluation -runner while keeping official temporal metrics and dataset logic reviewable in -the repository. This follows OpenAI's documented +three-condition Codex matrix and the two local-SLM conditions. It executes each +generated row, invokes its assertions, and stores provider responses, usage, +latency, cost, and traces in one local evaluation database. VidXP's Python test +generator selects the frozen tasks and conditions, creates and rotates the +repetitions, and attaches the assertions. VidXP's deterministic scorer computes +the per-case retrieval metrics; the repository reporter pairs and aggregates +them, and the export wrapper sanitizes Promptfoo's native export. The local +Pydantic-AI/Ollama adapter follows Promptfoo's documented +[Python provider contract](https://www.promptfoo.dev/docs/providers/python/). +This follows OpenAI's documented [Codex evaluation workflow](https://learn.chatgpt.com/use-cases/ai-app-evals). Promptfoo is not needed to choose a component model from published leaderboards. @@ -53,8 +107,8 @@ constraint, not just against generic eval feature lists: | Harness | Decision for this experiment | | --- | --- | -| Promptfoo Codex SDK | Selected: directly reuses Codex login, forwards per-provider Codex/MCP configuration, repeats paired cases, and captures usage and tool traces | -| Native Codex SDK/CLI | Capable, but would require custom pairing, retry, aggregation, and report plumbing that Promptfoo already provides | +| Promptfoo Codex SDK | Selected: directly reuses Codex login, forwards each condition's Codex/MCP configuration, executes the generated cases, invokes assertions, and stores usage and tool traces | +| Native Codex SDK/CLI | Capable, but would require direct SDK execution, assertion integration, result persistence, and trace storage that Promptfoo already provides | | [Inspect AI](https://inspect.aisi.org.uk/) | Stronger for portable research evals, but subscription-authenticated Codex requires a custom bridge rather than its standard model path | | [EvalBench](https://github.com/GoogleCloudPlatform/evalbench) | Supports MCP scenarios, but its documented Codex path is API-key oriented and its simulated-user turns would add runs not needed here | | [Harbor](https://github.com/harbor-framework/harbor) | Strong containerized agent benchmark infrastructure, but heavyweight and credential/API oriented for this local pilot | @@ -74,7 +128,7 @@ dataset revision `18889b01886e30c36b0d1c650ac4439ad460ee73`, the archive is `c83d62557f102c6d41ea95c2c3b3581657481c8646cc70b1e12a85ead27a7ae3`, and contains 28 videos. The annotation file is 4,522,592 bytes. -Only these five videos are indexed for the first ten-task pilot: +Only these five videos are indexed for the ten-task development and pilot set: | Video ID | Seed coverage | | --- | --- | @@ -98,104 +152,596 @@ therefore does not replace LongVALE in this ablation. ## Prepare the isolated environment -Promptfoo 0.122.2 requires Node.js 22.22.0 or newer. The benchmark-local -`.npmrc` enforces that requirement so an unsupported runtime fails during -installation instead of failing after Codex runs have begun. You also need -`uv` and the Codex CLI on `PATH`. The setup verifies FFmpeg and ffprobe and, -when they are absent, installs them through a supported package manager. On a -fresh macOS machine, install Homebrew before running setup so VidXP can install -FFmpeg automatically. +Promptfoo 0.122.2 requires Node.js 22.22.0 or newer. On macOS and Linux, the +benchmark runner selects a compatible Node installation automatically, +including Homebrew's versioned Node 22 installation. You do not need to change +`PATH` in each terminal. You also need `uv` and the Codex CLI on `PATH`. The +setup verifies FFmpeg and ffprobe for VidXP and, when they are absent, installs them +through a supported package manager. On a fresh macOS machine, install Homebrew +and `node@22` before running setup. Setup can then install FFmpeg automatically +when needed. From the repository root, run the automated setup: -```powershell -npm --prefix benchmarks/codex-mcp run setup +```bash +./benchmarks/codex-mcp/run setup --machine-id mac-m2-01 ``` The command installs the pinned Python and Node dependencies, creates isolated -state outside the checkout, initializes the system media runtime, opens Codex -login when authentication is absent, downloads and verifies the pinned -LongVALE archive, copies the five pilot videos, prepares the four required -capabilities, indexes the media, saves the evaluation environment in the -ignored `benchmarks/codex-mcp/.env` file, and runs preflight. Accept the -LongVALE dataset terms before running it. Do not copy or commit the generated -`auth.json`. +state and separate condition homes outside the checkout, installs the committed +VidXP evidence skill only in the VidXP workspace, initializes the system media +runtime, opens Codex login +when authentication is absent, downloads and verifies the pinned LongVALE +archive, links the same five pilot videos into the two non-VidXP workspaces, +prepares the four required capabilities, indexes the media, saves the evaluation +environment in the ignored `benchmarks/codex-mcp/.env` file, and runs preflight. +Accept the LongVALE dataset terms before running it. Do not copy or commit the +generated `auth.json`. `--machine-id` selects the stable ID defined in the +[metric database](metric_database.md#machines-used); it is stored in `.env`, so +rerunning setup does not require an export or another flag. Add a new machine +to that table before assigning it a new ID, and replace `mac-m2-01` in the +example when running elsewhere. By default, mutable state goes under the operating system's user data directory. Set only `VIDXP_EVAL_ROOT` when it needs to live elsewhere: ```powershell $env:VIDXP_EVAL_ROOT = 'D:\vidxp-eval' -npm --prefix benchmarks/codex-mcp run setup +npm --prefix benchmarks/codex-mcp run setup -- --machine-id win-hp-01 ``` The setup is safe to rerun. Cached downloads and prepared models are reused, -and indexing is skipped when all five videos and four modalities are already -present. The benchmark pins the Codex SDK directly and omits Promptfoo's -unrelated optional provider packages from the install. +including VidXP Desktop's existing model cache when it is present. Set +`VIDXP_MODEL_CACHE` before setup to select another prepared cache. Indexing is +stored in a directory named for the `INDEX_SCHEMA_VERSION` read from VidXP, so +a schema change rebuilds derived benchmark data without deleting the preceding +index. Indexing is skipped when all five videos and four modalities are already +present. Setup stops every VidXP worker still using its isolated benchmark +state before applying the configuration; durable jobs remain recoverable. The +saved model-cache path is passed explicitly into the benchmark's MCP process +with model downloads disabled, so the process uses the same prepared artifacts +that setup verified. The benchmark pins the Codex SDK directly and omits +Promptfoo's unrelated optional provider packages from the install. + +### Measure indexing separately + +The agent ablation intentionally starts from an existing index. Measure its +offline cost with three fresh, isolated index builds: + +```bash +./benchmarks/codex-mcp/run indexing +``` + +Pass another positive repetition count only when needed. This command reuses +the prepared pinned model cache with downloads disabled, rotates video order, +and records import and four-modality indexing time per video, whole-run time, +real-time factor, index bytes, and aggregate statistics. It removes only its +own temporary data and index directories; it does not modify the prepared index +used by the agent runs. The path-free JSON result is written under +`docs/benchmarking/runs/` for review, then linked from the +[metric database](metric_database.md#offline-indexing-measurements). It can take +substantially longer than the agent smoke because it rebuilds every modality +for all five videos in every repetition. ## Validate before spending runs Setup finishes by running preflight, which verifies the dedicated Codex -authentication, absence of ambient MCP configuration, all five media files, -the index paths, and a real VidXP MCP handshake. To repeat the configuration and -preflight checks without setup or Codex inference, run: +authentication, separate condition homes, absence of ambient MCP configuration, +skill and clean-PATH isolation, all +five media files in the direct-local and clean-user conditions, their exact +durations, the absent VidXP-on source paths, the repository machine ID, and the +index paths. It then starts the +exact configured VidXP MCP process, checks required tools and prepared models, +and verifies that every pilot video is ready and indexed for all four +modalities. This makes a missing or incorrectly forwarded model cache fail +before a Codex run. To repeat the checks without setup, Codex inference, or +VidXP model inference, run: + +```bash +./benchmarks/codex-mcp/run check +./benchmarks/codex-mcp/run preflight +``` -```powershell -npm --prefix benchmarks/codex-mcp run check -npm --prefix benchmarks/codex-mcp run preflight +The first paid/allowance-consuming smoke is one task in all three conditions: +three Codex runs total. + +```bash +./benchmarks/codex-mcp/run smoke ``` -The first paid/allowance-consuming smoke is one task in both conditions: two -Codex runs total. +Inspect all outputs and their trajectories before continuing. This first set +is development data: after any prompt, skill, tool, or scorer change, exclude it +from quality claims. The pilot command skips that task and runs the remaining +three repetitions by default: nine tasks Ɨ three conditions Ɨ three repetitions, +or 81 Codex runs total. +Condition order rotates across repetitions so serial timing does not always put +the same condition first or last. Repetition is already part of this one +evaluation command; do not invoke it three times manually. The report aggregates +all repetitions by condition and prints the per-run rows with `--all`. + +```bash +./benchmarks/codex-mcp/run pilot +``` -```powershell -npm --prefix benchmarks/codex-mcp run eval:smoke +Pass a positive repetition count when a larger variance sample is worth the +additional time and Codex allowance. For example, five repetitions are one +135-run evaluation, not five manual pilot invocations: + +```bash +./benchmarks/codex-mcp/run pilot 5 ``` -Inspect both outputs and their trajectories before continuing. The pilot command -runs ten tasks in two conditions with three repetitions: 60 Codex runs total. +After the matched pilot is frozen, a VidXP-only intervention may be run without +spending another direct-local or clean-user control: -```powershell -npm --prefix benchmarks/codex-mcp run eval:pilot +```bash +./benchmarks/codex-mcp/run vidxp +``` + +This runs the same nine held-out tasks and three repetitions, but only the +`codex-vidxp` condition. It is intended for a declared follow-up intervention, +not a replacement paired pilot. Compare it with the frozen controls by run ID +and report that the condition was measured later rather than counterbalanced in +the same evaluation. + +The separate local-agent lane compares two one-request policies using the +approved self-hosted Ollama model: + +```bash +./benchmarks/codex-mcp/run slm-smoke +./benchmarks/codex-mcp/run slm +``` + +Run `slm-smoke` first. It exercises both policies on one development task; it +is a runtime gate, not a research result. `slm` then runs each policy over the +nine held-out tasks three times by default, for 54 cases total. Both use the +same task generator, output schema, scorer, prepared index, and evidence +attestation as VidXP-on. Promptfoo's Python-provider API passes the adapter the +whole test row, including fields used later for scoring. The adapter ignores the +rendered benchmark prompt and selects only four public inputs: video ID, event +query, media filename, and target chunk size. It never reads the correct time +range or expected modalities. Only the event query and the condition's separate +system prompt reach the local model. This small, auditable input selectionā€”not +Promptfoo itselfā€”prevents the answer key from affecting retrieval. + +| Condition | Separate system prompt | One model request chooses | Fixed harness behavior | +| --- | --- | --- | --- | +| `local-slm` | [Modality router](../../benchmarks/codex-mcp/prompts/local-slm-router.txt) | Relevant modalities only | Preserve the event query; use VidXP's default `candidate_top_k=100`; return final `top_k=3` | +| `local-slm-planner` | [Search planner](../../benchmarks/codex-mcp/prompts/local-slm-planner.txt) | Search query, relevant modalities, and `candidate_top_k` from 3 through 100 | Return final `top_k=3`; the lower bound equals the output count and the upper bound equals the product default | + +After that request, the harness resolves the media with `list_media`, submits +one `search_moments` job, waits for its durable result, reads +`get_job_evidence`, expands each returned point or span to the shared bounded +ten-second serving window, and copies the first three distinct ready tiles in +VidXP rank order. It does not use labels, inspect media, rerank evidence, or ask +the model to interpret the result. The fixed result count prevents a top-one +default from hiding useful lower-ranked evidence. + +Promptfoo stores each detailed evaluation in its normal local database. +`run view` opens Promptfoo's UI, `run results` invokes VidXP's database-backed +reporter, and `run export` invokes VidXP's sanitizing wrapper around Promptfoo's +native JSON export. Before Promptfoo creates an +evaluation, preflight validates both structured prompts and discovers the four +harness-owned MCP tools. That wiring check uses Pydantic AI's test model, so it +makes neither a local-model request nor an MCP tool call. The separate MCP +preflight verifies the prepared models and five indexed videos. First run +`uv run --no-sync vidxp local-answers prepare --yes`. The benchmark starts the +saved local runtime when needed and stops only the process it started. It does +not download or substitute a model. The first provider call includes a managed +runtime cold start when Ollama was not already running; later calls in that +Promptfoo worker reuse it. Neither the router nor the harness has terminal or +source-media access. LongVALE's video ID is a dataset filename stem, not VidXP's +stable media ID, so the harness resolves the manifest filename through +`list_media` before searching. This keeps identity resolution on the public MCP +contract instead of hiding a benchmark-only ID map. + +This is targeted retrieval planning, not a tool-discovery or general agent +test. Each concise system prompt defines the four modalities and its exact +planning role. Neither loads the general-purpose Codex skill. The router leaves +`candidate_top_k` unset, which means 100; the planner may vary it; both fix +final `top_k` to the shared three-candidate contract. + +All benchmark MCP processes explicitly disable VidXP's optional internal query +model. This prevents `query_video` from making hidden model calls after a local +runtime has been installed. In both local-SLM conditions, the metered local +model makes only the planning decision; VidXP remains the evidence backend and +ranking system. + +The provider returns Ollama input/output tokens, local model-request count, and +its MCP item list; Promptfoo stores those fields and measures the complete +provider-call latency. Provider charge and external-agent calls are zero; +memory, energy, and local compute cost are unmeasured. The benchmark +and product share one Ollama model factory and request settings. They disable +reasoning, use typed JSON-schema output, and send the response allowance +through Ollama's supported +[`max_tokens` field](https://docs.ollama.com/api/openai-compatibility). The +model-derived request settings are the Qwen non-thinking recommendations: +32,768 maximum output tokens, temperature `0.7`, top-p `0.8`, and presence +penalty `1.5`. Ollama starts the managed runtime with its recommended 64,000 +agent context, Flash Attention, an 8-bit KV cache, and one parallel request. The +local provider records both requested and actually loaded context in the +Promptfoo response; a smaller loaded context invalidates the run instead of +silently recreating the earlier 4K failure. + +The local adapter uses Pydantic AI's +[native structured-output mode](https://ai.pydantic.dev/output/#native-output). +The protocol permits exactly one model request and no output retry. Query-length +and candidate-depth bounds are enforced by Pydantic after generation instead of +being compiled into the grammar; this avoids the documented llama.cpp +[`maxLength` grammar failure](https://github.com/ggml-org/llama.cpp/issues/26596) +without weakening validation. The remaining safety guards are a 180-second +limit on that model request and a 900-second Promptfoo Python-provider timeout +on the whole case. VidXP's durable search is observed through repeated, +maximum-30-second `wait_job` +calls until it reaches a terminal state. Neither timeout limits video duration. +Provider-returned failures retain model usage, selected modalities, MCP +activity, and bounded MCP error text; an outer Promptfoo timeout necessarily +kills the provider before it can return those fields. + +Both commands finish with a comparison of pass counts, temporal IoU, recall at +each IoU threshold, boundary errors, elapsed time, average and total token usage +and cost, +model turns, skill loading, and Promptfoo-recorded MCP and shell tool calls. +Token reporting separates +total input, cached input, uncached input, output, and reasoning tokens. Reasoning +is included in output. The report preserves Promptfoo's supplied cost unchanged. +The harness pins Promptfoo +[0.122.2](https://www.npmjs.com/package/promptfoo?activeTab=versions), the npm +`latest` release when rechecked on September 7, 2026. Promptfoo records Codex +input as reported by the SDK, with cached input as a subset rather than an +additional token count; total tokens are input plus output, and reasoning is a +subset of output. This is the corrected behavior after +[issue #7546](https://github.com/promptfoo/promptfoo/issues/7546), which affected +0.120.23. + +For configured `gpt-5.6-sol`, the pinned standard estimator uses $5 per million +uncached input tokens, $0.50 per million cached input tokens, and $30 per million +output tokens while total input is at most 272,000 tokens. Above that threshold, +the whole row uses $10, $1, and $45 respectively. The requested model is explicit, +so Promptfoo does not have to infer it. The Codex SDK does not expose cache-write +token counts; as Promptfoo's +[provider documentation](https://www.promptfoo.dev/docs/providers/openai-codex-sdk/) +warns, that can understate the estimate. The dollar value is therefore a +same-version, same-estimator comparison metric, not an end-user price, API +invoice, or measured Codex-plan charge. The 81 current-pilot rows reproduce +their stored costs from these saved token fields and rates. + +Print the latest saved comparison again, without inference, with: + +```bash +./benchmarks/codex-mcp/run results +``` + +Add `--all` to include every per-run interval in a full pilot report. Add +`--responses` to print each final answer, returned modalities, source job, and +evidence count. The report also shows agent runs, model turns, total recorded +items, model requests, all tool calls, VidXP MCP calls, and shell calls. Counts +come from the items saved in each Promptfoo provider response; the report does +not infer tool use from command text. Codex supplies its recorded items, and the +local provider records its actual MCP calls and local request count. For VidXP +runs, it +also reads each saved job and reports fused retrieval R@1, R@3, and R@5, the top +fused interval, its constituent hits, and the best retained hit per modality. +This exposes what fusion actually used and which fused rank retained each hit; +it does not rerun retrieval. +Candidates removed by the current pre-fusion or final `top_k` cannot be +reconstructed from the saved job, and the report states that limitation. Use +`--no-retrieval` only when the saved VidXP jobs are unavailable. + +After a scorer or media-duration correction, add `--rescore` to re-audit saved +responses and traces against the current scorer and validated task durations, +without calling Codex or another model. This requires the original VidXP +durable jobs and labels the output as a current deterministic audit; it does +not overwrite the at-run Promptfoo scores in the retained export. + +The `trace` command remains as an explicit alias for inspecting the same saved +retrieval details: + +```bash +./benchmarks/codex-mcp/run trace +``` + +This reads saved jobs only. It reports each boundary and its IoU against the +task annotation without starting Codex, invoking a model, or rerunning the +benchmark. + +Before comparing localization methods, export all indexed records and scores +for the task's declared action, scene, sound, and speech modalities: + +```bash +./benchmarks/codex-mcp/run probe TASK_ID --top-k 3 +``` + +Unlike `trace`, this command performs one local text-embedding inference per +declared modality and queries every indexed record for that video. The output +keeps each modality's raw distance, rank, interval, representation metadata, +model identity, runtime, and call count separate; it does not pretend the +scores are calibrated across models. It writes JSON under the ignored +benchmark state directory. The report includes the reconstructed current +fusion and IoU/boundary errors plus top-retrieved and best-individual-record +IoU per modality, and prints those measurements directly after the run. The +best-individual value is a diagnostic oracle, not a production prediction. The +command does not invoke Codex or change production search. + +After all ten probes exist, replay their saved rankings at independent +pre-fusion depths while holding the final result depth at ten: + +```bash +./benchmarks/codex-mcp/run depth +``` + +This makes no model, Codex, or API calls. It prints R@1, evidence-board R@3, +R@5, and R@10 curves and saves the per-task candidates beside the probes. The +tested depths are diagnostic samples, not proposed defaults. The control shows +whether candidate collection loses a match and whether fusion keeps separate +moments bounded. + +Compare a saved probe with the benchmark-only Point-to-Span ASG adaptation: + +```bash +./benchmarks/codex-mcp/run compare TASK_ID ``` +This performs no model calls. It prints the control and adapted interval, IoU, +boundary errors, runtime, and per-modality candidate counts, then saves the +full method record beside the probe. The saved development result concluded +this diagnostic: it improved the coarse union but used only the sound curve and +remained below direct media inspection. Do not run the held-out agent batch for +this adaptation alone. + +Build and compare one isolated overlapping VideoPrism action representation: + +```bash +./benchmarks/codex-mcp/run representation TASK_ID \ + --sample-fps 4 \ + --stride-samples 8 +``` + +VideoPrism always receives 16 sampled frames. This example therefore produces +nominal four-second windows every two seconds. The four-second size is a fixed- +window control evaluated by Point-to-Span; the 50% overlap is a VidXP experiment +setting, not a parameter copied from that paper. The command requires both +values, builds a separate action-only index, reuses the saved scene, sound, and +speech probe, and reports action retrieval, fused IoU, indexing time, index +bytes, record count, and query time. It makes no Codex calls, but it does run +VideoPrism indexing and one action text embedding. Confirm before running it. + +After every held-out action task has a saved probe, compare the current, +fine-only, and coarse-to-fine action paths with: + +```bash +./benchmarks/codex-mcp/run representation --held-out \ + --sample-fps 4 \ + --stride-samples 8 +``` + +The coarse-to-fine control keeps a four-second record when its midpoint lies +inside any top-three eight-second result. It preserves the fine similarity +order and returns one record without union. The report includes top-1 IoU, +top-three and full-list candidate recall, per-task intervals and ranks, index +cost, and model-call counts. It makes no Codex or API calls. + +Reproduce the concluded disjoint shot-proposal control: + +```bash +./benchmarks/codex-mcp/run shots TASK_ID +``` + +This implements the no-postprocessing ShotDetect path from Diwan et al. with +their published PySceneDetect content threshold `53`. It runs PySceneDetect +`0.7` through OpenCV, reuses the saved 1 fps SigLIP2 curve, and ranks each shot +by its best contained scene score. A separate VidXP-only result applies the +existing RRF score to each fixed shot using the best overlapping top-three rank +from every other saved modality; those hits can change the proposal rank but +cannot expand its boundary. The command reports both results, oracle proposal +IoU, recall thresholds, proposal count, and detection time. It makes no model +calls and writes no index. The paper used CLIP-ViT-B/32 and sampled within each +shot; the report records both VidXP adaptations and excludes SimpleWatershed. + +After exporting fresh probes for tasks 3ā€“10, aggregate the held-out local +comparison: + +```bash +./benchmarks/codex-mcp/run shots --held-out +``` + +The command preserves the manifest's declared modalities. It compares scene +ranking with proposal-preserving RRF only where scene is declared, reports the +two action-and-sound tasks separately, counts evidence that overlaps multiple +proposals, and states whether each reference crosses a detected boundary. Shot +detection makes no model calls; the required probes for this pilot make 16 +local text-embedding calls in total. This is not a Promptfoo or Codex run. + +Compare the saved full-query rankings with a frozen manual wording ceiling and +FineLAP's clip/frame paths: + +```bash +./benchmarks/codex-mcp/run queries +``` + +This command uses the eight saved held-out probes as the baseline, makes 32 +local text-embedding calls, and writes one ignored JSON report. It does not run +Codex or Promptfoo. The manual phrases use only content stated in the task query +and are not an automatic planner result. For sound, the report separately ranks +FineLAP's whole-window and dense-activation records; it does not invent a final +merge rule. + +Check the historical FineLAP selector on the four held-out sound tasks: + +```bash +./benchmarks/codex-mcp/run sound +``` + +This runs the exact full-query product path, then one local diagnostic pass per +task to report global-gate coverage, final activation coverage and rank, IoU, +boundary errors, time, and model/vector-call counts. It makes no Codex, +Promptfoo, or API call. + +Open the saved local results in Promptfoo's browser interface without running +another evaluation: + +```bash +./benchmarks/codex-mcp/run view +``` + +The viewer opens `http://localhost:15500` and continues running until you press +`Ctrl-C`. + +### Preserve a reviewed run + +Promptfoo's local database contains the full interactive run, but it is not +portable or committed. Every evaluation command passes `--no-share`, so it does +not upload the evaluation snapshot or media through Promptfoo sharing. This is +separate from Promptfoo's default basic usage telemetry, which its +[telemetry documentation](https://www.promptfoo.dev/docs/configuration/telemetry/) +says excludes prompts, outputs, test cases, provider keys, and full configuration. +Set `PROMPTFOO_DISABLE_TELEMETRY=1` when that basic telemetry must also be +disabled; do not describe `--no-share` alone as an air-gap. After reviewing a +run, preserve its latest evaluation +with: + +```bash +./benchmarks/codex-mcp/run export +``` + +Pass one or more evaluation IDs after `export` to preserve older runs. The +command uses Promptfoo's native JSON export, removes Codex raw response bodies, +session IDs, secrets, and personal paths, and writes an importable artifact to +`docs/benchmarking/runs/`. It retains the prompt and provider configuration, +final responses, scores, usage, and traces; the compact local-SLM MCP item list +is retained because it is that provider's auditable tool record. Import one into +a separate Promptfoo database with +`npm --prefix benchmarks/codex-mcp run promptfoo -- import --new-id` +when the full UI is needed. + +Every newly generated test row records `VIDXP_EVAL_MACHINE_ID`, and the export +wrapper repeats that stable ID at `metadata.vidxpExport.machineId`. Machine +hardware and software are defined once in the metric database instead of copied +into every large Promptfoo artifact. + Promptfoo Community and the repository's Python evaluation code are no-cost open-source software. The local MCP server and local VidXP processing create no OpenAI or Anthropic inference charge, but downloading and indexing consume local bandwidth, disk, electricity, and any paid infrastructure the operator chooses; the dataset and model licenses still apply. Codex inference authenticated -through the dedicated ChatGPT login consumes the account's Codex plan allowance -or credits. API-key authentication instead incurs API usage charges. No +through the dedicated ChatGPT login consumes the account's +[Codex plan allowance or credits](https://developers.openai.com/codex/pricing); +the dollar column is Promptfoo's provider estimate for comparison, +not a measured plan charge or invoice. If a run uses API-key authentication, +actual charges must come from the provider's billing records. No LLM-as-judge assertion is enabled, so this scaffold does not add grader calls. -The run count is therefore exactly two for the smoke and 60 for the pilot. +The run count is therefore exactly three for the development smoke and 81 for +the default held-out pilot; an explicit repetition override changes only the +pilot count. Promptfoo reports usage, but it cannot determine the remaining ChatGPT-plan allowance or convert subscription-authenticated runs into an exact dollar charge; use the Codex account usage display for that limit. -## Scoring and interpretation +The recorded development runs are summarized in +[Benchmark results](results.md#codex-mcp-development-smoke). It is retained to +diagnose the harness and current temporal behavior, not as held-out evidence. -Each response must identify one interval. The deterministic scorer records -temporal IoU, R@1 at tIoU 0.3/0.5/0.7, interval validity, and whether the expected -MCP boundary was respected. Report at least: +## Scoring and interpretation -- success rate and mean IoU by condition; +Each task asks for one event and up to three distinct candidate clips, ordered +most to least likely. Each clip targets 10 seconds and accepts 8ā€“12 seconds. +Success@3 requires at least one clip to cover half of the annotated event that +can fit in 10 seconds. This lets a fixed window containing a short event pass +while rejecting two-second blinks, whole-video answers, and unbounded result +lists. Returning fewer than three candidates is valid. The window and result +limit are VidXP product-evaluation policies, not LongVALE metrics. +The scorer rejects exact duplicate intervals but does not impose an arbitrary +overlap threshold because legitimate windows can overlap the same event. +VidXP candidates share one retrieval job; the agent is not required to launch +more searches or inspect every artifact to fill the list. + +The deterministic scorer retains Success@1, reciprocal rank, candidate count, +top-one and best-of-three temporal IoU, R@1 and R@3 at tIoU 0.3/0.5/0.7, +start/end/duration error for the first candidate, interval validity, and whether +the expected VidXP boundary was respected. Promptfoo's Codex provider saves the +SDK item trajectory and emits trace spans for shell and MCP operations. Skill +loading is only its documented heuristic for direct `SKILL.md` reads, not a +first-class Codex skill event. The report counts tools from saved provider items, +falling back to trace spans, and retains tool names, order, and inputs. Because +the provider does not retain MCP result bodies, the scorer uses the returned +source job ID to verify the authoritative result directly in VidXP's durable job +store. It also matches +each candidate's evidence IDs and modalities to ready evidence from that job, +then verifies that its interval overlaps the delivered evidence range. + +The report also scores VidXP's visible evidence separately from the agent's +answer. MCP surfaced-target Hit@1 and Hit@3 ask whether a ready evidence tile +shown by `get_job_evidence` covers the same half-event threshold. These retrieval +diagnostics do not require an 8ā€“12-second final clip and do not enter the paired +product gate. They distinguish ā€œVidXP found and exposed itā€ from ā€œthe agent +selected and returned it.ā€ + +Attestation requires only the evidence IDs because the durable job already owns +their intervals and metadata. The agent may use the initial board, metadata, +keyframes, or clips and inspect an artifact only when that resolves a mismatch +or uncertainty. Any extra inspection still counts toward time, tokens, and tool +calls. + +Report at least: + +- bounded-chunk Success@3, Success@1, reciprocal rank, and candidate count; +- VidXP MCP surfaced-target Hit@1 and Hit@3; +- top-one and best-of-three IoU plus R@1/R@3 at tIoU 0.3/0.5/0.7; - results by scene, action, sound, speech, and joint-modality task; -- token usage, latency, failures, and retries; -- VidXP MCP tool trajectories for MCP-on; +- input/cached/uncached/output/reasoning token usage, Promptfoo-supplied + comparison cost, latency, failures, agent runs, and model turns; +- skill and VidXP MCP tool trajectories for VidXP-on; - indexing time, index size, model preparation, and machine details; and - every excluded or failed task. -Do not call the ten-task pilot a LongVALE result. A publishable result requires -the complete official evaluation split, its one-interval output conversion, and -the official evaluator. A centralized benchmark would additionally need frozen -agent versions, provider-independent authentication, portable environments, and -public result governance. - -The MCP-off condition is intentionally a local-agent baseline, not a native -video-model benchmark. The Codex SDK accepts text and local images but does not -accept video or audio inputs directly. With the network disabled and the -workspace read-only, MCP-off may use installed read-only shell inspection tools -but cannot call VidXP or persist extracted media. Report this limitation with -the results; component-model quality remains covered by the published benchmark -record elsewhere in this collection. +The scorer binds each durable result to the query the agent actually submitted; +it does not require a verbatim copy of the user's wording. Inspecting a clip +delivered by that job remains VidXP use, while opening the source media directly +is a condition violation. + +The report never applies the product gate to a development smoke. For the pilot, +every matched VidXP/direct-local pair must first be condition-valid and +scorable. Otherwise the gate is not scored and any valid-pair comparison is +diagnostic only. With complete pairs, the gate passes only when VidXP matches +or improves bounded-chunk Success@3 and uses fewer total tokens. The clean-user +condition is supporting evidence. Latency, cost, calls, boundary quality, and +all three raw summaries remain visible; the verdict does not replace them. + +Evaluation +[`eval-7VR-2026-09-06T10:58:07`](runs/eval-7VR-2026-09-06T10-58-07.json) +completed the isolated held-out pilot in all three conditions. The current +deterministic scorer accepts all 81 saved runs. The agent-level gate failed, +while VidXP's visible top three evidence tiles surfaced the target on 19/27 +VidXP runs. See [Benchmark results](results.md#current-codex-mcp-held-out-pilot) +for the measurements and interpretation. + +The fixed prompt already asks for up to three grounded candidates and says not +to reconfirm evidence that already supports one. The shipped skill now makes +the handoff precise: for a requested shortlist, preserve each distinct ready +candidate from the initial ranked evidence up to three, dropping only failures, +duplicates, or evidence-confirmed mismatches. It does not require opening or +parsing every artifact. In the saved run, five final misses had a qualifying +visible top-three tile and seven did not. The `vidxp` command measures this +declared handoff intervention against frozen controls without rerunning them. +That comparison can support an intervention analysis, but it must not be called +the original counterbalanced product gate. + +Selected earlier runs remain as diagnostics, not product-gate evidence. The +exact-interval runs preserve the failure and later boundary behavior; +`eval-2uz-2026-09-05T17:39:13` is a bounded two-condition smoke; and +`eval-YDK-2026-09-05T20:29:45` established that a tool-free third lane cannot +inspect the media. Their artifacts and valid conclusions are linked from the +[metric database](metric_database.md#historical-agent-runs). + +Do not call the nine-task held-out pilot a LongVALE result. A publishable result +requires the complete official evaluation split, its one-interval output +conversion, and the official evaluator. A centralized benchmark would +additionally need frozen agent versions, provider-independent authentication, +portable environments, and public result governance. + +The VidXP-off condition is intentionally the same local agent without VidXP. It +is not required to use FFmpeg, inspect a particular artifact, or follow a +prescribed call sequence. The clean-user condition instead begins without +third-party host executables but may obtain its own tools. It measures bootstrap +behavior, not a native video model. Component-model quality remains covered by +the published benchmark record elsewhere in this collection. diff --git a/docs/benchmarking/benchmark_catalog.md b/docs/benchmarking/benchmark_catalog.md index f31614ad..ca7c3167 100644 --- a/docs/benchmarking/benchmark_catalog.md +++ b/docs/benchmarking/benchmark_catalog.md @@ -47,9 +47,10 @@ The selected suite remains component-based: LongVALE is the strongest peer-reviewed combined visionā€“audioā€“speech temporal benchmark found. It still omits actor clustering and expects genuinely fused -multi-modal interval predictions. FineLAP now supplies separate global-window and -dense timestamped sound evidence, but the LongVALE adapter and fusion rule remain -unimplemented and no quality score is claimed. FLARE is a smaller downloadable +multi-modal interval predictions. PE-A-Frame supplies timestamped sound-frame +evidence through bounded overlapping sections, but the complete LongVALE +adapter and quality run remain pending. No LongVALE quality score is claimed. +FLARE is a smaller downloadable audio-visual stress test, but it is a 2026 preprint benchmark with generated, filtered queries. It belongs in a secondary experiment or watchlist until peer review and benchmark stability improve. @@ -159,7 +160,7 @@ their published numbers alone do not answer a VidXP capability question: | 5 | Dialogue | TVR, `t` subset | A, medium adapter | Gated | Lawful TV clips with original audio | | 6 | Actor | BCL on BBT/Buffy | A, medium clustering adapter | Gated | Released inference script cannot score VidXP clusters; lawful raw episodes needed | | 7 | Visual | Charades-STA | A, medium adapter | Gated | Dataset agreement and narrow staged domain | -| 8 | Whole system | LongVALE | A/medium fusion adapter | Artifacts reachable; compliance/runtime gates | Evaluation-only raw archives are 40.523 GiB; full 254 GB repository is not required; FineLAP sound records are available, while the evaluation adapter remains | +| 8 | Whole system | LongVALE | A/medium fusion adapter | Artifacts reachable; compliance/runtime gates | Evaluation-only raw archives are 40.523 GiB; full 254 GB repository is not required; PE-A-Frame sound indexing is integrated, while its long-audio gate and the evaluation adapter remain | | 9 | Whole system | FLARE | A/medium adapter | Ready artifacts; runtime gate/watchlist | 66.267 GiB release; preprint; generated rank-filtered queries; visual/audio/joint coverage now needs adapter validation | | 10 | Actor | Hannah | A, medium evaluator adapter | Gated | Research agreement and separately obtained movie | | 11 | Actor/system | MovieNet | A for component slices | Gated | Registration; movies excluded; actor labels are keyframe-oriented | @@ -542,8 +543,8 @@ The active provider conclusions and exact published selection scores are in speech, and generic audio but not actors. VidXP must freeze a point-to-interval or interval-proposal rule and emit one top-ranked interval, then combine its visual, environmental-sound, and speech evidence with a frozen, - provenance-preserving fusion rule. The FineLAP provider now exists, but that - fusion adapter does not. + provenance-preserving fusion rule. The PE-A-Frame provider exists, but that + complete evaluation adapter does not. Returning top three alone does not satisfy the protocol. Generic-audio evidence within official event queries is unsupported by the current implementation; keep all 13,867 queries in the denominator unless a separately justified @@ -734,8 +735,8 @@ Completed: Next: -1. Complete a bounded real-media FineLAP integration smoke, retaining LAION-CLAP - as the mature comparison. +1. Run the PE-A-Frame long-audio product gate with the documented section and + evidence defaults. 2. Implement the fixed LongVALE visual/sound/speech adapter and validate one evaluation archive before committing to the full 1,171-video run. 3. Add fixed hardware-aware indexing and query measurements to each subsequent diff --git a/docs/benchmarking/execution_readiness.md b/docs/benchmarking/execution_readiness.md index 6fe43e22..fa09feaa 100644 --- a/docs/benchmarking/execution_readiness.md +++ b/docs/benchmarking/execution_readiness.md @@ -2,8 +2,8 @@ > **Historical assessment:** Statements below that generic sound was unsupported > accurately describe the implementation when this assessment was written. The -> active [multimodal model direction](model_selection.md) now records the shipped -> FineLAP sound layer and places LongVALE and FLARE adapter validation next. +> active [multimodal model direction](model_selection.md) records the PE-A-Frame +> replacement and the remaining long-audio validation work. Collection index: [Benchmarking research](README.md) diff --git a/docs/benchmarking/metric_database.md b/docs/benchmarking/metric_database.md new file mode 100644 index 00000000..f23cf65c --- /dev/null +++ b/docs/benchmarking/metric_database.md @@ -0,0 +1,436 @@ +# VidXP metric database + +Last verified: 2026-09-07 + +This is the public index of VidXP's measured results. Each result identifies the +research protocol or method it tests, VidXP's deviation from that work, the +machine used, and the conclusion the evidence supports. The tables contain the +relevant measurements; they do not depend on one maintainer's local files. + +Use [published comparison results](published_results.md) for other systems' +reported scores and [research adoption](research_adoption.md) for the smaller +list of ideas accepted into VidXP. Scores below use proportions from `0` to `1` +unless a percent sign is shown. + +## Find the evidence you need + +| Need | Section | +| --- | --- | +| Read the result summary first | [Evidence at a glance](results.md#evidence-at-a-glance) | +| Understand the benchmark question, controls, and pass rule | [Research question and protocol](#research-question-and-protocol) | +| Identify the evaluation machine and software | [Machines used](#machines-used) | +| Review Codex, VidXP, direct-local, clean-user, and local-SLM results | [Whole-system agent measurements](#whole-system-agent-measurements) | +| Check indexing cost excluded from agent time | [Offline indexing measurements](#offline-indexing-measurements) | +| Review model, modality, fusion, and ranking experiments | [Component and ranking measurements](#component-and-ranking-measurements) | +| Find official adapter results | [Official adapter measurements](#official-adapter-measurements) | +| Open retained run artifacts and reproduction inputs | [Evidence retained in the repository](#evidence-retained-in-the-repository) | +| See which measurements are still missing | [Measurements still required](#measurements-still-required) | + +## Research question and protocol + +The whole-system benchmark asks whether giving the same Codex agent VidXP's +already-indexed video evidence preserves useful retrieval while reducing agent +tokens and, ideally, elapsed time. It does not ask VidXP to trim a two-second +event into a two-second deliverable. + +| Item | Fixed protocol | +| --- | --- | +| Evidence unit | Return up to three distinct 8ā€“12-second clips in ranked order. Success@3 requires at least one clip to cover half of the annotated event that can fit in 10 seconds. Returning fewer candidates is valid. | +| Data | Ten selected, LongVALE-derived tasks over five videos, covering scene, action, sound, speech, and joint evidence. The development smoke uses the first task; the held-out pilot uses the remaining nine. This is not an official LongVALE score. | +| Timed starting state | Direct-local and clean-user receive hard links to the same media bytes. VidXP-on receives the index built from those bytes but no source-media path in its workspace. All five videos start indexed for scene, action, sound, and speech. Dataset download, model preparation, media import, and indexing are outside agent time. | +| Comparison | Same Codex model, reasoning effort, neutral user prompt, output schema, and fresh state. VidXP-on has the shipped skill and MCP; direct-local has system commands plus host FFmpeg and ffprobe but no VidXP; clean-user starts with OS tools plus terminal and network. | +| Decision | Across every matched, condition-valid pilot pair, VidXP must match or improve direct-local bounded-chunk Success@3 and use fewer total agent tokens. Missing, contaminated, or unscorable primary pairs make the gate unscored. Success@1, rank, latency, Promptfoo cost, calls, IoU, R@K, and boundary errors remain visible. | +| Repetition | The pilot defaults to three repetitions with rotated serial condition order. Per-run values, means, totals, and failures are retained. | +| Machine identity | Every new test row and repository export carries a stable repository ID such as `mac-m2-01`. The table below defines that ID; no hardware serial number or host-generated UUID is stored. | +| Offline cost | Indexing is measured separately on fresh isolated indexes. The agent benchmark must not hide that cost or add it to only the VidXP-on response time. | +| Required isolation | Separate workspaces and homes prevent state reuse. A Codex permission profile denies filesystem-root access and reopens only minimal runtime paths, the current condition workspace, andā€”for direct-localā€”the FFmpeg installation prefix. On macOS, preflight tests those OS-enforced boundaries before model calls. Blocked path attempts are allowed agent behavior; the scorer rejects actual cross-condition capability use. | + +Two declared follow-ups reuse the frozen controls rather than repeating them. +`./benchmarks/codex-mcp/run vidxp` measures a VidXP-only evidence-handoff +intervention; it is not counterbalanced with the earlier controls. +`./benchmarks/codex-mcp/run slm` compares two targeted, one-request local +policies over the same held-out tasks and scorer: + +| Local condition | Model chooses | Harness fixes | +| --- | --- | --- | +| Router | Modalities | Original query, product-default candidate depth of 100, and three final results | +| Planner | Search query, modalities, and candidate depth from 3 through 100 | Three final results; the range spans the output count to the product default | + +Both use the managed loopback model and separate +[router](../../benchmarks/codex-mcp/prompts/local-slm-router.txt) and +[planner](../../benchmarks/codex-mcp/prompts/local-slm-planner.txt) system +prompts, then follow the same +deterministic MCP lifecycle. `list_media` resolves the public dataset filename +to VidXP's stable media ID; no benchmark-only identity map is hidden from the +model. The harness expands returned evidence points or spans into the shared +ten-second serving window, but it does not use labels, inspect media, or rerank +VidXP output. + +VidXP's generator owns task selection, conditions, repetition rows and their +rotated order; its scorer and reporter own deterministic metrics, pairing, and +aggregation. Promptfoo executes those rows, invokes the assertions, measures +provider-call latency, and stores results and traces. The local provider returns +its tokens, one model request per case, and MCP items for Promptfoo to retain; +its declared provider charge and external-agent calls are zero. Memory, energy, +and local compute cost remain unmeasured. Every benchmark MCP process disables +VidXP's optional internal query model, so reported usage cannot omit nested SLM +requests. Run `./benchmarks/codex-mcp/run slm-smoke` as a one-task runtime gate +before the held-out comparison; do not include that smoke in quality claims. + +For Codex rows, Promptfoo's cost is a pinned estimator over SDK-reported tokens, +not a measured plan charge. Cached input is a subset of input, reasoning is a +subset of output, and missing cache-write counts can understate cost. Every eval +disables Promptfoo result sharing; its separate basic usage telemetry excludes +benchmark prompts and outputs. The exact rates, limits, and opt-out are in the +[agent-ablation method](agent_ablation.md#why-promptfoo-is-the-execution-harness). + +The task design comes from +[LongVALE](https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.pdf); +the practical ten-second serving unit and product gate are VidXP evaluation +choices. See [agent ablation](agent_ablation.md) for the executable method and +[research adoption](research_adoption.md) for paper-derived product decisions. + +## Machines used + +| ID | Hardware | Software and execution | Applies to | +| --- | --- | --- | --- | +| `mac-m2-01` | MacBook Pro `Mac14,7`; Apple M2; 8 CPU cores (4 performance, 4 efficiency); 10 GPU cores; 8 GB memory; ARM64 | macOS 15.6.1 (`24G90`); Python 3.14.7; PyTorch 2.13.0; Transformers 5.14.1; ChromaDB 1.5.9; NumPy 2.5.1; FFmpeg 8.1.1; Node.js 22.23.2; Promptfoo 0.122.2. VidXP selected CPU; PyTorch reported neither MPS nor CUDA available. | September 2026 agent and component rows. Selected exports carry this stable ID; the hardware/software definition remains centralized here. The ID on older exports is a retrospective assignment, not a machine snapshot captured by those runs. | +| `win-hp-01` | HP ENVY Laptop 16-h0xxx; Intel Core i7-12700H; 14 cores, 20 logical processors; 15.72 GiB memory; NVIDIA RTX 3060 Laptop GPU with 4 GiB VRAM | Windows 11; Python 3.14.0; PyTorch 2.13.0+cpu; Transformers 5.14.1; Sentence Transformers 5.6.1; ChromaDB 1.5.9. The GPU was present but unused. | July 2026 official-adapter rows. Current-provider manifests contain this snapshot; surviving legacy artifacts do not contain every package or immutable model revision. | + +## System evaluated + +| Evidence | Provider and revision | Product representation | Research boundary | +| --- | --- | --- | --- | +| Speech | faster-whisper `large-v3-turbo@0a363e9` and Qwen3 Embedding `0.6B@97b0c61` | Timestamped transcript segments | [Whisper](https://arxiv.org/abs/2212.04356) supplies transcription and [Qwen3 Embedding](https://arxiv.org/abs/2506.05176) supplies semantic retrieval. Benchmarks that provide transcripts do not test transcription. | +| Scene | SigLIP 2 `base-patch16-224@75de2d5` | Frames sampled at 1 fps | [SigLIP 2](https://arxiv.org/abs/2502.14786) supplies image-text similarity. It does not predict scene or event boundaries. | +| Action | VideoPrism `lvt-base-f16r288@fb6de9f` | Sixteen-frame clips sampled at 2 fps, normally about eight seconds | [VideoPrism](https://arxiv.org/abs/2402.13217) supplies global video-text embeddings. VidXP's fixed windows and raw long-video ranking are not the paper's action-localization method. | +| Sound | PE-A-Frame Small `e5fc71c`; FineLAP `b419aa2` benchmark control | PE-A frames are indexed every 40 ms through ten-second inference sections with two-second overlap, then reduced to distinct ten-second evidence windows at search time. | [PE-AV](https://arxiv.org/abs/2512.19687) establishes the model and dot-product frame score. Sectioning, midpoint overlap ownership, and evidence windows are configurable VidXP controls, not paper-derived settings. | +| Fusion | No model | Rank-anchored candidates with at most one directly overlapping hit per supporting modality | [RRF](https://doi.org/10.1145/1571941.1572114) defines `sum(1 / (60 + rank))`. Candidate construction and interval union are VidXP rules; indirect overlap cannot join separate moments. | + +Full immutable revisions are pinned in the +[speech](../../src/vidxp/capabilities/speech/specs.py), +[scene](../../src/vidxp/capabilities/scene/specs.py), +[action](../../src/vidxp/capabilities/action/specs.py), and +[sound](../../src/vidxp/capabilities/sound/specs.py) specifications. A row below +states when an experiment replaces these normal representations. + +## Agent product metrics + +| Metric | Definition | Role and research boundary | +| --- | --- | --- | +| Bounded-chunk Success@3 | At least one of up to three ordered 8ā€“12-second results covers `0.5` of `min(annotation duration, 10 seconds)` | Primary per-task product retrieval metric. The ten-second target and three-result limit are VidXP serving choices, not LongVALE metrics. They reject blink-length, whole-video, and unbounded-list answers. | +| Success@1 and reciprocal rank | Whether the first clip succeeds, and `1 / first successful rank` | Exposes ordering quality without making a top-one miss erase useful evidence returned immediately after it. | +| MCP surfaced-target Hit@1/Hit@3 | Whether a ready evidence tile exposed by `get_job_evidence` covers `0.5` of `min(annotation duration, 10 seconds)` within the first one or three tiles | VidXP-only retrieval diagnostic. It separates evidence availability from the agent's final selection and does not replace the cross-condition bounded-clip gate. | +| Paired product gate | VidXP-on Success@3 is at least VidXP-off, and VidXP-on uses fewer total agent tokens | Primary whole-system decision. Candidate count, cost, latency, and calls remain reported separately, so returning more clips does not hide its overhead. | +| Temporal IoU and R@1/R@3 at tIoU 0.3/0.5/0.7 | Exact predicted intervals against the LongVALE-derived annotation | Retained secondary boundary-quality diagnostics. Poor exact trimming and ordering remain product shortcomings and future research targets. | +| Local-SLM router Success@3 | One local structured-output request selects modalities; the harness submits the unchanged query with default candidate depth and delivers VidXP's top three as bounded windows | Tests minimal local routing plus VidXP retrieval without an external model. | +| Local-SLM planner Success@3 | One local structured-output request selects the search query, modalities, and candidate depth; the harness delivers VidXP's top three as bounded windows | Tests whether limited local planning improves retrieval. Neither local condition tests general MCP use, evidence interpretation, answer synthesis, or a paired Codex product gate. | + +The two older September development runs used the earlier exact-interval prompt. +The later smoke and first pilot used one bounded clip. The current isolated run +uses the ranked three-candidate contract above. Historical results are not +rescored as if their agents had been allowed to return three clips. + +## Input integrity checks + +| Check | Machine | Result | Decision | +| --- | --- | --- | --- | +| LongVALE-derived sound references | `mac-m2-01`; PCM levels measured over each exact reference interval and all annotations for the engine video checked | Siren RMS/peak `-18.83/-3.75 dBFS`; engine `-19.22/-2.81`; phone `-91.75/-78.27`; drumbeat `-39.60/-17.18`. The phone video matches the downloaded archive at SHA-256 `0468c1bde02a752d1f20ab370556e59768a5da19519ea1cfe4a0e9760fd5b2f7`. The engine video has several annotated rev/roar intervals; WSTAG's 242.22 s top lies inside the separate 241.760ā€“243.554 s rev annotation. | The custom sound-only score is invalid for phone and engine. Keep the original intervals in the collective LongVALE tasks, where visual and action details disambiguate them; report the sound limitation instead of changing the multimodal labels. | + +## Whole-system agent measurements + +The agent runs compare the same Codex model with VidXP MCP evidence, direct +local inspection, and a clean-user bootstrap condition. VidXP-on begins with +the five pilot videos already indexed in all four modalities; all agent times +exclude download, preparation, import, and indexing. + +### Current isolated held-out pilot + +Evaluation +[`eval-7VR-2026-09-06T10:58:07`](runs/eval-7VR-2026-09-06T10-58-07.json) +completed 81 runs: nine tasks, three conditions, and three repetitions on +`mac-m2-01`. Wall time was 11,621.136 seconds, or 3 h 13 min 41.136 s. The +table uses the current deterministic rescore of the saved responses, traces, +and durable VidXP jobs; it makes no new model calls. + +| Condition | Quality | Efficiency | Recorded activity | +| --- | --- | --- | --- | +| VidXP | 27/27 valid and scorable; Success@3 `15/27`; Success@1 `15/27`; visible MCP evidence Hit@3 `19/27`, Hit@1 `9/27` | 81.423 s and 236,060 tokens per run; 6,373,630 tokens total; $10.894431 Promptfoo estimate | 266 model turns; 186 tools: 158 MCP and 28 shell; 27 skill loads | +| Direct local | 27/27 valid and scorable; Success@3 `18/27`; Success@1 `18/27` | 99.669 s and 297,310 tokens per run; 8,027,375 tokens total; $14.702576 estimate | 310 model turns; 169 shell tools | +| Clean user | 27/27 valid and scorable; Success@3 `16/27`; Success@1 `16/27` | 247.446 s and 697,139 tokens per run; 18,822,764 tokens total; $36.348442 estimate | 592 model turns; 421 shell tools | + +Against direct local inspection, VidXP used 20.6% fewer tokens and was 18.3% +faster on average. It used fewer tokens in 20/27 matched pairs, was faster in +19/27, and had a lower Promptfoo comparison cost in 19/27. Its Success@3 was +lower by `3/27` or 11.1 percentage points, so the product gate **failed**. +Agents returned only 1.15 VidXP candidates on average, so agent Success@3 +equalled Success@1. The visible MCP evidence +result is a separate product diagnostic: Hit@3 `19/27` versus Hit@1 `9/27`. +Fourteen runs both surfaced and returned a hit, five surfaced one without +returning a qualifying final clip, one returned a hit outside the visible +top-three metric, and seven did neither. Thus 5/12 final-answer misses expose an +agent-selection opportunity, while 7/12 still require better retrieval or +ranking. This does not convert the failed paired gate into a pass. + +### Local-SLM held-out comparison + +Evaluation +[`eval-BSO-2026-09-06T22:02:53`](runs/eval-BSO-2026-09-06T22-02-53.json) +completed 54 cases on `mac-m2-01`: nine tasks, two policies, and three +repetitions. Wall time was 1,343.293 seconds, or 22 min 23.293 s. All cases were +condition-valid and scorable, returned exactly three candidates, made one local +model request, and incurred no external-provider charge. + +| Policy | Success@3 | Success@1 | MRR | Best@3 IoU | Average time | Average tokens | MCP calls | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| Router | 15/27 (`55.6%`) | 10/27 | `.432` | `.2143` | 21.765 s | 155 | 149 | +| Planner | 19/27 (`70.4%`) | 11/27 | `.537` | `.2498` | 25.544 s | 270 | 164 | + +The planner gained four successful runs while adding 3.779 seconds and 115 +tokens per case. It rewrote all 27 queries and selected candidate depths of 10 +(8 runs), 20 (9), 30 (1), 35 (1), or 50 (8). The router preserved all queries +and used the product default. Its modalities exactly matched the manifest's +annotation channels in 23/27 runs; the planner did so in 17/27. Exact channel +agreement is diagnostic, not a target: the planner still produced more useful +ranked windows. + +| Held-out task | Codex + VidXP final | Router | Planner | +| --- | ---: | ---: | ---: | +| Bell plus title | 3/3 | 3/3 | 3/3 | +| Car plus siren | 0/3 | 0/3 | 0/3 | +| Engine rev | 0/3 | 0/3 | 2/3 | +| Sketching | 3/3 | 3/3 | 3/3 | +| Office speech | 2/3 | 0/3 | 2/3 | +| Signing | 1/3 | 3/3 | 2/3 | +| Phone ring | 1/3 | 3/3 | 3/3 | +| Stir and cover | 3/3 | 3/3 | 3/3 | +| Casserole drumbeat | 2/3 | 0/3 | 1/3 | +| **Total** | **15/27** | **15/27** | **19/27** | + +The planner produced at least one success for every task on which the Codex + +VidXP agent succeeded, but it did not improve every repetition: drumbeat fell +from 2/3 to 1/3. Its 19/27 final-window score equals the earlier agent run's +19/27 visible-evidence count numerically, but these are different measurements. +The local run's original evidence tiles reached the raw-boundary threshold in +15/27 cases; expanding points and short spans into the fixed ten-second serving +window produced four additional final-window successes. The retained artifact +contains every route, rewritten query, depth, result, token count, MCP call, and +score needed to audit that distinction. + +The full export predates a label-only provider fix: planner responses say +`agentRole=modality-router`, while their condition and +`instructionProfile=targeted-search-planner-v1` identify the executed planner. +The corrected smoke records `search-planner`. This metadata correction does not +change prompts, retrieval, usage, or scores, so the held-out run was not repeated. + +On this selected pilot, the planner's 70.4% final-window score is comparable to +direct local inspection's 66.7% and exceeds the Codex + VidXP agent's 55.6%. +Its average was 25.544 seconds and 270 local tokens versus 81.423 seconds and +236,060 Codex tokens for the VidXP agent. This supports the narrower claim that +one loopback planning request can preserve pilot retrieval quality while +removing external-agent token cost. It does not establish general accuracy, +measure local compute cost, test evidence interpretation, or repair the car and +remaining ranking misses. + +### First held-out pilot audit + +Evaluation +[`eval-dxR-2026-09-06T00:15:35`](runs/eval-dxR-2026-09-06T00-15-35.json) +completed 81 runs: nine tasks, three conditions, and three repetitions on +`mac-m2-01`. Wall time was 10,524.855 seconds, or 2 h 55 min 24.855 s. The raw +Promptfoo artifact preserves the at-run scores; the table below is the current +deterministic re-audit of its saved responses, traces, and VidXP jobs. +This historical pilot required one final candidate, so its hits are Success@1; +it cannot be rescored as though the agents returned three. + +| Condition | Validity and quality | All-run efficiency | Recorded activity | +| --- | --- | --- | --- | +| VidXP | 18/27 condition-valid and scorable; 9/18 bounded hits. Before validity filtering: 16/26 scorable outputs hit. | 92.625 s and 276,456 tokens per run; 7,464,325 tokens total; $13.647699 Promptfoo estimate | 305 model turns; 258 tools: 192 MCP and 66 shell; 27 skill loads | +| Direct local | 25/27 condition-valid and scorable; 12/25 bounded hits. Before validity filtering: 14/27 hit. | 110.877 s and 301,162 tokens per run; 8,131,363 tokens total; $17.334364 estimate | 346 model turns; 327 shell tools | +| Clean user | 22/27 condition-valid and scorable; 15/22 bounded hits. Before validity filtering: 18/27 hit. | 184.389 s and 527,971 tokens per run; 14,255,209 tokens total; $29.677447 estimate | 494 model turns; 315 shell tools | + +Only 17/27 VidXP/direct-local pairs remained both condition-valid and +scorable. On those pairs, VidXP achieved 8/17 hits versus 7/17, averaged +194,499 versus 263,239 tokens, and averaged 70.045 versus 94.565 seconds. +Average Promptfoo estimates were $0.312777 versus $0.588837. +Those are diagnostics, not a product win: excluding 10 pairs can bias both +quality and efficiency. The product gate is therefore **not scored**. + +The primary exclusions were seven VidXP runs that inspected source media, +one without a source job, and one whose returned evidence did not belong to +that job. Two direct-local runs read prior benchmark artifacts outside their +workspace; one overlaps a VidXP-invalid pair. Two clean-user runs reached host +developer-tool paths and three read repository or prior benchmark state; that +supporting lane does not enter the primary gate. The scorer fix +made eight legitimate agent query paraphrases valid, distinguished one +VidXP-delivered clip inspection from source-media bypass, and corrected all +five manifest durations to the indexed media values. The duration correction +restored a 4 ms end-of-video answer. Future +runs also omit the direct source path from VidXP-on instead of relying only on +post-run exclusion. + +The earlier pilot remains unscored. The replacement root-denied permission +profile and preflight boundary probes were added afterward, so a new smoke must +confirm all three model conditions before the paid pilot is repeated. + +Across 26 recoverable VidXP source jobs, fused retrieval at tIoU 0.5 was +6/26 at R@1 and 14/26 at R@3 and R@5. Useful candidates therefore reached the +top three more often than rank one; final ordering remains the clearest product +failure exposed by this run. Exact boundaries also remain weak. No provider, +fusion, or serving-window change was made from this result alone. + +### Development smoke + +The retained development run uses one +[LongVALE](https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.pdf)-derived +task with reference interval `0ā€“6` seconds. It proves the harness and exposes +product behavior; one task is not a LongVALE score or a held-out quality +estimate. + +| Evaluation | Machine | VidXP | Direct local | Clean user | Valid conclusion | +| --- | --- | --- | --- | --- | --- | +| [`eval-0eL-2026-09-05T22:40:10`](runs/eval-0eL-2026-09-05T22-40-10.json) | `mac-m2-01` | `0ā€“12` s; hit `1`; coverage `1`; IoU `.500`; 72.888 s; 221,139 tokens; 9 turns; 6 Promptfoo-recorded tools; $0.317147 | `0ā€“10` s; hit `1`; coverage `1`; IoU `.600`; 93.591 s; 373,984 tokens; 16 turns; 7 recorded tools; $0.755479 | `0ā€“10` s; hit `1`; coverage `1`; IoU `.600`; 245.755 s; 860,165 tokens; 30 turns; 26 recorded tools; $1.572180 | Corrected three-condition development smoke. VidXP matched the primary result with 40.9% fewer tokens and 22.1% lower latency than direct local inspection. Product gate not scored. | + +The VidXP job ranked `0ā€“10` seconds first with action, scene, and sound support. +The agent expanded its answer to `0ā€“12`, which accounts for the lower answer +IoU. Promptfoo's Codex provider supplies time, tokens, cost, recorded items, and +tool types. The report reads Codex rollout token events only for the internal +model-turn count. + +The local-agent lane has its own one-task runtime gate because it is not part of +the paired Codex comparison: + +| Evaluation and policy | Machine and model | Result | Usage | Valid conclusion | +| --- | --- | --- | --- | --- | +| [`eval-Com-2026-09-06T22:35:55`](runs/eval-Com-2026-09-06T22-35-55.json), router | `mac-m2-01`; Ollama `qwen3.5:4b-q4_K_M`; 64,000 loaded context | PASS; `scene,sound`; three candidates; Success@1/Success@3 `1`; top result `0ā€“10` s | 36.798 s; 155 tokens; one model request; five MCP calls; $0 external-provider charge | Validates the fixed router, bounded-window output, scoring, and accounting. | +| Same evaluation, planner | Same machine, model, and context | PASS; `scene,sound`; candidate depth 50; three candidates; Success@1/Success@3 `1`; top result `0ā€“10` s | 43.789 s; 292 tokens; one model request; six MCP calls; $0 external-provider charge | Validates the separate planner prompt and variable query/modality/depth path. | +| [`eval-pBj-2026-09-06T20:27:51`](runs/eval-pBj-2026-09-06T20-27-51.json) | `mac-m2-01`; same model and context | Three candidates; Success@3 `0`; top result `30ā€“40.006` s | 396.854 s; 25,918 tokens; seven model requests; four MCP calls | Superseded multi-turn diagnostic. It showed why the model should plan once instead of executing and copying the MCP lifecycle. | + +The combined smoke took 82.914 seconds wall time. Both policies passed without +reference data in their prompts. Its development task is excluded from the +held-out quality comparison above. + +### Historical agent runs + +All rows below predate the 2026-09-06 neutral-prompt and state-isolation fix. +The prompt named VidXP or its absence, and conditions reused one Codex home, so +their quality and efficiency deltas are retained only as debugging history. +They cannot support an ablation claim. + +| Evaluation | Machine | VidXP-on | VidXP-off | Efficiency comparison | Valid conclusion | +| --- | --- | --- | --- | --- | --- | +| [`eval-2uz-2026-09-05T17:39:13`](runs/eval-2uz-2026-09-05T17-39-13.json) | `mac-m2-01` | `0ā€“10` s; bounded hit `1`; coverage `1`; IoU `.6000`; 78.660 s; 200,142 total tokens; 52,458 uncached input; 1,636 output; 5 MCP calls; $0.384394 estimate | `0ā€“10` s; bounded hit `1`; coverage `1`; IoU `.6000`; 90.582 s; 277,660 total tokens; 27,837 uncached input; 2,527 output; 7 shell calls, 6 through FFmpeg/ffprobe; $0.639381 estimate | VidXP used 77,518 fewer tokens, 11.922 fewer seconds, and a $0.254987 lower provider estimate; uncached input was 24,621 higher | Both found the same useful fixed window. Historical bounded-clip diagnostic only; the baseline prompt was contaminated. | +| [`eval-J6s-2026-09-01T19:30:07`](runs/eval-J6s-2026-09-01T19-30-07.json) | `mac-m2-01` | `0ā€“8.0075` s; IoU `0.7493`; 74.552 s; 301,712 total tokens; 48,423 uncached input; 1,769 output; 6 MCP calls; $0.815355 provider estimate | `0ā€“6.8` s; IoU `0.8824`; 112.209 s; 329,961 total tokens; 35,906 uncached input; 3,623 output; 10 media shell calls; $0.812527 estimate | VidXP used 28,249 fewer tokens and 37.657 fewer seconds, but more uncached input made its estimate $0.002828 higher. | Both found the event. Historical boundary diagnostic only; the baseline prompt was contaminated. | +| [`eval-mw5-2026-09-02T19:40:44`](runs/eval-mw5-2026-09-02T19-40-44.json) | `mac-m2-01` | `0ā€“10` s; IoU `0.6000`; 79.647 s; 261,995 total tokens; 48,523 uncached input; 1,760 output; 7 tools, including 6 MCP calls; $0.401271 estimate | `0ā€“6.81` s; IoU `0.8811`; 89.757 s; 313,617 total tokens; 56,950 uncached input; 3,227 output; 9 media shell calls; $0.968155 estimate | VidXP used 51,622 fewer tokens, 10.110 fewer seconds, two fewer tools, and a $0.566884 lower estimate. | Superseded global-only FineLAP diagnostic. The ten-second result rejects a global sound window as the final boundary; it does not measure current two-stage sound search. | +| [`eval-jJD-2026-09-01T17:51:57`](runs/eval-jJD-2026-09-01T17-51-57.json) | `mac-m2-01` | `64.031ā€“75.809` s; IoU `0`; 89.030 s; 229,415 total tokens; $0.353389 estimate | `0ā€“6.8` s; IoU `.8824`; 72.650 s; 207,110 total tokens; $0.307287 estimate | VidXP used 22,305 more tokens and 16.380 more seconds | Failed historical ranking diagnostic. It exposed the sound tokenization/integration defect later fixed in `343bd27`; it is not current product evidence. | +| [`eval-YDK-2026-09-05T20:29:45`](runs/eval-YDK-2026-09-05T20-29-45.json) | `mac-m2-01` | `0ā€“10` s; hit `1`; IoU `.600`; 78.249 s; 273,865 total tokens; $0.751985 estimate | `0ā€“10` s; hit `1`; IoU `.600`; 120.378 s; 244,632 total tokens; $0.398351 estimate | VidXP used 29,233 more tokens and 42.129 fewer seconds | Harness-design diagnostic only. Its tool-free third lane could not inspect media, so that lane was rejected and replaced by the clean-user bootstrap condition. | + +Historical dollar values are Promptfoo's supplied provider estimates. The +report preserves them unchanged. Use them only to compare conditions using the +same pinned Promptfoo version and model configuration; they are not measured +subscription charges or invoices. Reasoning tokens are already included in +output tokens. + +## Offline indexing measurements + +Index construction is a separate systems benchmark because users pay it before +search while the agent comparison measures work after the index exists. + +| Protocol | Measurement | Current status | +| --- | --- | --- | +| Five pilot videos totalling 914.789 seconds; scene, action, sound, and speech; prepared pinned model cache; model downloads disabled; fresh data and index directories for each repetition; sequential CLI path matching benchmark setup | Per-video import, four-modality indexing, combined time, indexing real-time factor, total wall time, and final index bytes. Report every repetition plus mean, median, sample standard deviation, minimum, and maximum. | Not run. Use `./benchmarks/codex-mcp/run indexing` for three repetitions. The command does not touch the prepared agent index and writes a path-free JSON artifact under `docs/benchmarking/runs/` for review and later linkage here. | + +The first repetition may benefit less from operating-system file cache than the +later ones, so raw repetitions stay visible; averages do not erase that order +effect. This measures the existing product CLI path, including process and +model load inside each per-video index command. It excludes dataset download, +model preparation, agent inference, and search. This is resource accounting, +not a paper-derived ranking method or an accuracy score. + +## Component and ranking measurements + +These controls use frozen LongVALE-derived tasks and `mac-m2-01`. They make no +Codex or API calls. ā€œCandidate recallā€ asks whether a usable interval exists in +the returned list; it does not mean that VidXP selected that interval. + +| Experiment and research basis | Scope and cost | Result | What it establishes | +| --- | --- | --- | --- | +| `p2s_asg_vidxp_v1`; [Point-to-Span](https://arxiv.org/abs/2512.10363), Section 3.1 | One development task; saved score curves; no model calls | Previous union IoU `0.7493`; adapted interval `0.64ā€“6.72` s and IoU `0.7976`; direct-inspection IoU `0.8824` | The adaptive sound span helped, but the partial adaptation produced no scene or action span and remained below direct inspection. It is concluded, not adopted. | +| `videoprism_overlap_control_v1`; [CTAP](https://openaccess.thecvf.com/content_ECCV_2018/html/Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.html) and [long-video guidance](https://openaccess.thecvf.com/content/ICCV2023/html/Barrios_Localizing_Moments_in_Long_Video_Via_Multimodal_Guidance_ICCV_2023_paper.html) motivate candidate coverage | Five action tasks; normal 79 records versus 307 four-second records; five text embeddings; fine index took 1,175.579 s and wrote 5,966,316 bytes | Eight-second top-1 mean IoU `0.0680`, R@1 at tIoU 0.5 `0`; four-second top-1 mean IoU `0.1297`, R@1 at tIoU 0.5 `.20`, top-3 candidate recall `.40`, full-list recall `.60`; top-three coarse gating reduced full-list recall to `.40` | Overlap improves candidate availability, but raw VideoPrism similarity and the tested gate do not rank it reliably. CTAP's learned proposal ranking and boundary adjustment were not implemented. | +| `diwan_shotdetect_siglip2_v1`; [Off-the-Shelf VMR](https://proceedings.mlr.press/v203/diwan23a.html) | Eight tasks; 16 text embeddings; 46.744 s probe generation; 16.923 s shot detection; no model calls or index writes for detection | Development shot IoU `0.8902`. Held out: current union mean IoU `0.0418`; best-shot oracle `.5219`; on six scene-comparable tasks, scene ranking `.2841` versus proposal RRF `.1175` | Shot boundaries can supply useful candidates. VidXP's proposal RRF harmed ranking; five tasks were boundary-limited and three ranking-limited at tIoU 0.5. The paper's CLIP plus SimpleWatershed pipeline was not reproduced. | +| `manual_modality_query_ceiling_v1`; query decomposition is compared with, not claimed from, [Zero-Shot VMR](https://openaccess.thecvf.com/content/WACV2024/html/Luo_Zero-Shot_Video_Moment_Retrieval_From_Frozen_Vision-Language_Models_WACV_2024_paper.html) | Eight tasks; 16 task-modality pairs; 32 text embeddings; 13.590 s | Target overlap in top 3 changed `7/16` to `8/16`; best-boundary record in top 3 changed `4/16` to `7/16`; nine overlap ranks improved and two worsened | Manual modality wording is an upper-bound control, not the paper's full method. Mixed results reject mandatory rewriting. | +| `finelap_separate_streams_v1`; [FineLAP](https://aclanthology.org/2026.acl-long.473/), Sections 3.2ā€“3.3 | Four sound tasks within the preceding query control | A target appeared in a top-three list on `0/4` tasks when global and dense records were mixed and `3/4` when the streams were ranked separately | The original mixed ranking was invalid. The result supports separate representation paths, not VidXP's final global-then-local selector. | +| `finelap_two_stage_runtime_2026-09-03`; [FineLAP](https://aclanthology.org/2026.acl-long.473/) representations with VidXP orchestration | One real Apple Silicon query through FineLAP, Chroma, the application, fusion, and JSON output | Dense result `1.60ā€“2.08` s with parent context `0ā€“10` s | Real-path smoke only. It proves the current selector executes; it supplies no held-out IoU or comparative quality evidence. | +| `finelap-two-stage-held-out@eae7000`; [FineLAP](https://aclanthology.org/2026.acl-long.473/), Sections 3.2ā€“3.3, plus VidXP's selector | Four designated intervals; full frozen application queries; top 3; eight local text embeddings including diagnostic duplication; 5.172 s total | Global gate coverage `2/4`; final activation top-1 and top-3 coverage `0/4`; full gated activation coverage `2/4`; final mean IoU `0`; R@1 at tIoU 0.3/0.5/0.7 all `0`; surviving target ranks `132` and `63` | Exact diagnostic retained, but phone and engine invalidate it as a provider decision. It does not decide whether the paired multimodal run can proceed. | +| `candidate-depth-fusion-control-v1`; [RRF](https://doi.org/10.1145/1571941.1572114) ranking over current VidXP temporal groups | All ten frozen collective tasks; saved full-query modality rankings; depths 1, 3, 5, 10, 20, 50, 100, 250, 500, 1,000, and all; final depth 10; no model or API calls | From depth 3 to 20, board R@3 at tIoU 0.5 rose `.30` to `.40` and output R@10 rose `.30` to `.40`, while R@1 stayed `.20`. At depth 100, R@1 fell to `0`; at full depth, every top result spanned nearly the whole video and all threshold rates were `0`. | Early truncation hides usable evidence, but a larger fixed depth is not the fix. Transitive overlap grouping turns denser input into video-length components. Candidate generation must be separated from final ranking before candidate depth can be selected. | +| `candidate-depth-direct-overlap-control-v2`; [RRF](https://doi.org/10.1145/1571941.1572114) over VidXP's corrected bounded candidates | `mac-m2-01`; the same ten frozen tasks and saved rankings; identical depth sweep; final depth 10; no model or API calls | Depths 100 through all produced identical rates. At full depth, R@1/R@3/R@5/R@10 at tIoU 0.5 were `.10/.10/.20/.20`; no top result expanded to the full video. | Direct overlap fixes the transitive-union failure. Low R@5 remains attributable to provider ordering and source-window boundaries, not depth collapse. | +| `pe-a-frame-small-mac-diagnostic`; [PE-AV](https://arxiv.org/abs/2512.19687), PE-A-Frame Small `e5fc71c1f0be50279f52f292390b589780079e13` | `mac-m2-01`; official Transformers implementation; F32 CPU; official threshold `0.3`; no API calls. One complete 73.14-second phone-ring track plus four label-centered clips. | Full track: 244.35 s, 4.30 GiB peak RSS, 125 predicted fragments, target miss. Target-aware clips: full-query mean best-span IoU `0.1654` and target score above surrounding audio `1/4`; sound-only mean `0.1151` and `0/4`. Best per-task full-query IoU: siren `0.0317`, engine `0.4615`, phone `0`, drumbeat `0.1682`. | Inconclusive for provider selection because two sound labels were invalid. Retained as a runtime and failure diagnostic; the AEGBench row below supersedes it for selection. | +| `aegbench-sound-seed42-n50`; [AEGBench](https://huggingface.co/datasets/zihan-audio/AEGBench) `49a1d919`, [PE-A-Frame Small](https://huggingface.co/facebook/pe-a-frame-small) `e5fc71c`, FineLAP `b419aa2` | `mac-m2-01`; 50 recordings sampled from all 3,425 manifest rows with seed 42; 149 categories with annotated intervals; two categories without intervals excluded explicitly; 613.43 seconds of audio; no API calls or test-set threshold tuning | PE-A/FineLAP frame AUROC `.8614/.8401`; frame AP `.7616/.7484`; top point inside an event `.7651/.7315`; default-threshold mean IoU `.5226/.2924`; R-IoU@0.5 `.5099/.2802`. Inference `183.30/17.98` s; real-time factor `.2988/.0293`; peak RSS `5.30/1.59` GB. | PE-A-Frame Small selected for sound localization because it wins every quality measure while remaining faster than playback. This subset decides the candidate, not a full AEGBench score or long-audio claim. | +| `pe-a-product-section-smoke-2026-09-05`; [PE-A-Frame Small](https://huggingface.co/facebook/pe-a-frame-small) `e5fc71c` | `mac-m2-01`; 75.81-second LongVALE development video; product decoder, model runtime, Chroma inner-product index, frame de-duplication, and search; no API calls | A 60-second-section control took `198.651` s. Ten-second sections took `23.356` s at zero overlap, `22.156` s at two seconds, and `33.564` s at five seconds; warmed-model timings are not a formal speed comparison. Every run stored 1,896 unique frames. Two-second overlap ranked the labelled opening event in `0ā€“10` s and ending bell in `70ā€“80` s. | Reject 60-second sections on this Mac. Ten seconds is the measured resource choice; two seconds is the smallest tested nonzero overlap and five seconds added cost without changing the checked results. The ten-second evidence window is a playable context unit, not a boundary claim. This one-video smoke is not general retrieval accuracy; the long-audio gate remains required. | +| `kinetics-mini-videoprism-2026-09-05`; [Kinetics](https://arxiv.org/abs/1705.06950) [five-class derivative](https://huggingface.co/datasets/nateraw/kinetics-mini) `9f4ed381`; VideoPrism `fb6de9f` | `mac-m2-01`; 50 ten-second validation videos; VidXP's 2 fps/16-frame records; five direct action prompts; no API calls | Top-1 `1.00` overall and for every class; 390.49 s total, 7.81 s/video. PE-AV Small 16-frame `9f888ee` classified one archery smoke correctly but took 13.36 s; its weights are 3,388,082,648 bytes. | Keep VideoPrism. The gate shows that basic action recognition works; it says nothing about exact long-video location. PE-AV supplies no interval head and offered no measurable quality headroom here. | +| `flexsed-mac-held-out`; [FlexSED](https://arxiv.org/abs/2509.18606) detector `eefe52b7ad686a9bc9f1f5dd0803e2c52171e128`, LAION CLAP `8fa0f1c6d0433df6e97c127f64b2a1d6c0dcda8a` | `mac-m2-01`; released non-overlapping ten-second path; 63 detector calls over 616.7 seconds of unique audio; full and sound-only wording; no API calls or tuned settings | Load `1.265` s; inference `10.854` s; peak RSS `1.57` GiB. Designated target score beat all surrounding frames on `0/4` full and `0/4` sound-only queries. Mean target-best frame percentile was `0.7962` full and `0.7439` sound-only. Published processing produced one designated-target overlap, engine at about `0.045` IoU. | Runtime passes, but the overall quality rate is invalid because phone is silent and engine has repeated valid matches. FlexSED missed both unique valid cases and is not selected; overlap cannot repair those raw misses. | +| `dasm-release-compatibility-2026-09-05`; [DASM](https://arxiv.org/abs/2507.16343), Transformer4SED `c3e883d0fbeaf7031b467d45a3c46a88a76c00b6` | `mac-m2-01`; read-only inspection of official source, inference notebook, requirements, and 636 MB model-hub tree; no API calls | Text inference sets `device = 'cuda'`, requires an external MGA-CLAP checkout and checkpoint, and uses hard-coded local paths. The Transformer4SED repository has no software license. | Blocked before execution; no quality or runtime score. MIT metadata on the model hub does not grant a license to copy the separate source implementation. | +| `wstag-audiocaps-v2-mac-held-out`; [WSTAG](https://arxiv.org/abs/2401.02584), model `c1ede4afca77acb67bbd20e48e3fc4657b96666a`, LAION CLAP `365dea6ef167def6676140ed93bbc43f84dabb28` | `mac-m2-01`; author-recommended post-paper 131.96M-parameter model; exact 528,030,960-byte weights; three audible designated intervals, full and sound-only wording; six whole-track CPU forwards over 1,679.9 input seconds; no API calls or tuned settings | Load `0.811` s from cache; inference `25.82` s; individual 247ā€“296 s tracks `3.42ā€“5.02` s; peak RSS `4.15` GiB. Designated target wins were `0/3` for either wording; mean target-best percentile `0.8688` full and `0.8985` sound-only. Designated-target IoU was zero at the released `0.5` threshold. The engine top at `242.22` s is inside another annotated rev interval (`241.760ā€“243.554` s). | Runtime passes. WSTAG missed the two unique valid cases and is not selected, but no overall provider score is claimed. The hub's advertised AutoModel path is broken; the diagnostic loaded the same class and exact weights with zero checkpoint mismatches. | +| `videoprism_provider_conformance_2026-09-03`; [VideoPrism](https://arxiv.org/abs/2402.13217) official preprocessing and checkpoint | One identical 16-frame tensor and six texts through official Flax and pinned Transformers implementations | Video and text embedding cosine parity rounded to `1.0`; every similarity score differed by less than `0.000051` | The port is numerically valid. Query canonicalization was fixed in `7e7d6c8`; the remaining failure is VidXP's global-similarity ranking design. | + +The action result motivates a trained sequence-to-interval grounder rather than +more fixed-window tuning. [HieraMamba](https://openaccess.thecvf.com/content/CVPR2026/html/An_HieraMamba_Video_Temporal_Grounding_via_Hierarchical_Anchor-Mamba_Pooling_CVPR_2026_paper.html) +establishes hierarchical long-video grounding, and +[UniversalVTG](https://arxiv.org/abs/2604.08522) applies it through one +cross-domain checkpoint. Neither release currently satisfies VidXP's CPU/Mac +and licensing gates; this is a documented direction, not a VidXP result. + +## Official adapter measurements + +These rows test dataset adapters and current or legacy providers. Smoke subsets +validate execution and evaluator compatibility, not provider quality. + +| Benchmark and research protocol | Machine | Scope | Result | Evidence status | +| --- | --- | --- | --- | --- | +| [DiDeMo](https://openaccess.thecvf.com/content_iccv_2017/html/Hendricks_Localizing_Moments_in_ICCV_2017_paper.html) | `win-hp-01` | Official test; 4,021 searches over 1,037 videos; legacy CLIP provider | Rank@1 `20.19%`; Rank@5 `55.71%`; mean IoU `34.60%` | Full legacy result. One corrupt official media object was replaced with a byte-matching archived copy, as recorded in [adapter validation](adapter_validation.md). | +| [HiREST](https://openaccess.thecvf.com/content/CVPR2023/papers/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.pdf) | `win-hp-01` | Official validation; 193 known-video searches; released transcripts and legacy MiniLM | R@0.5 `78.24%`; R@0.7 `44.56%` | Full validation result, not a held-out test score and not a transcription result. | +| [DiDeMo](https://openaccess.thecvf.com/content_iccv_2017/html/Hendricks_Localizing_Moments_in_ICCV_2017_paper.html) current-provider smoke | `win-hp-01` | Official test annotation index `0`; one video; current SigLIP 2 | Rank@1 `0`; Rank@5 `1`; mean IoU `0` | One-example real provider, storage, serialization, and official-evaluator check only. | +| [HiREST](https://openaccess.thecvf.com/content/CVPR2023/papers/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.pdf) current-provider smoke | `win-hp-01` | Two declared validation pairs over two videos; released transcripts and current Qwen3 | R@0.5 `.50`; R@0.7 `.50` | Two-example real provider, storage, filtering, serialization, and evaluator check only. | + +## Evidence retained in the repository + +The tables above are the public numeric record. The repository also retains the +inputs and code needed to understand or reproduce them: + +- the [LongVALE-derived task manifest](../../benchmarks/codex-mcp/tasks/longvale-part9-pilot.json), + [fixed agent prompt](../../benchmarks/codex-mcp/prompts/video-evidence.txt), + [local router prompt](../../benchmarks/codex-mcp/prompts/local-slm-router.txt), + [local planner prompt](../../benchmarks/codex-mcp/prompts/local-slm-planner.txt), + [Promptfoo configuration](../../benchmarks/codex-mcp/promptfooconfig.yaml), + and [reporter](../../benchmarks/codex-mcp/scripts/report.mjs); +- the action, proposal, query, sound, candidate-depth, and Point-to-Span controls under + `benchmarks/codex-mcp/scripts/`; +- the selected, sanitized, importable [Promptfoo run exports](runs/), including + the corrected smoke and historical diagnostics that changed direction; +- [current result interpretation](results.md), [paper validation](paper_validation.md), + and [published comparison results](published_results.md). + +Generated databases, media, indexes, model weights, raw Codex response bodies, +session IDs, secrets, and personal paths are not committed. The retained +Promptfoo exports preserve the remaining configuration, responses, scores, +usage, and traces. Local-SLM exports also retain their compact provider-recorded +MCP items; Codex raw response bodies remain omitted. + +## Measurements still required + +- Rebuild the sound index and run the PE-A-Frame long-audio product gate. The + provider and bounded section path are implemented, but the one-video smoke + does not validate hour-long or fused retrieval. +- Repeat the 81-run pilot only after a retrieval-ranking or agent-selection + change when a new matched, counterbalanced gate is required. The current + evidence-handoff intervention can instead use the selective `vidxp` run and + the completed controls, with that later-run limitation disclosed. +- Expand the completed local router/planner comparison beyond the selected + nine-task pilot before making a general local-equivalence claim. Memory, + energy, and local compute cost also remain unmeasured. +- Run the isolated three-repetition indexing benchmark and link its reviewed + JSON artifact from the offline-indexing table above. +- Produce full-corpus DiDeMo and HiREST results for the current providers. +- Add model revisions, peak memory, model-call counts, and raw-prediction + identity to future generated run manifests. New agent exports now carry the + stable machine ID; do not infer fields missing from historical execution. diff --git a/docs/benchmarking/modality_gates.md b/docs/benchmarking/modality_gates.md new file mode 100644 index 00000000..32bbda8a --- /dev/null +++ b/docs/benchmarking/modality_gates.md @@ -0,0 +1,159 @@ +# Individual modality gates + +Collection index: [Benchmarking research](README.md) + +Status: Sound candidate selected; complete scene, speech, and action corpus gates pending + +Last verified: 2026-09-05 + +These gates answer two different questions before the paid agent comparison: + +1. Does the current model and VidXP ranking path work on a task the model was + designed to perform? +2. Does that output fit VidXP's actual task: finding useful intervals in video? + +A native-task pass does not imply a product-task pass. Candidate providers must +use the same dataset, split, query set, and metrics as the current provider. + +## Required gates + +| Evidence lane | Current provider | Native or isolation gate | Product-task gate | Current state | +| --- | --- | --- | --- | --- | +| Scene | [SigLIP 2](https://arxiv.org/abs/2502.14786) | The existing [DiDeMo](https://github.com/LisaAnne/LocalizingMoments) adapter isolates sampled visual-frame ranking within one video | DiDeMo's fixed five-second moments measure whether those frame scores rank the described visual moment | Adapter complete; one current-provider smoke only | +| Action/video | [VideoPrism LvT](https://arxiv.org/abs/2402.13217) | [MSR-VTT 1K-A](https://github.com/m-bain/frozen-in-time) text-to-video retrieval checks the published global video-text use case and complete-corpus ordering | [Charades-STA](https://github.com/jiyanggao/TALL) checks whether VidXP's independently ranked eight-second action records find labelled action intervals | Both canonical adapters wired; a 50-video Kinetics-mini candidate gate scored 50/50 and retains VideoPrism, but does not replace either canonical gate | +| Environmental sound | [PE-A-Frame Small](https://huggingface.co/facebook/pe-a-frame-small) integrated; FineLAP retained as a control | [AEGBench](https://huggingface.co/datasets/zihan-audio/AEGBench) checks open-vocabulary frame ranking, repeated events, and interval output | [Clotho-Moment](https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/) or [CASTELLA](https://arxiv.org/abs/2511.15131) checks text-to-interval retrieval over long audio | Identical 50-recording/149-query comparison selected PE-A-Frame; product-path sectioning smoke passes; long-audio quality gate pending | +| Speech meaning | Qwen3 Embedding | HiREST with released transcripts isolates transcript chunking, embedding, and timestamp ranking | The same HiREST known-video moment task scores whether the relevant spoken procedure is localized | Adapter complete; two-pair current-provider smoke only | +| Transcription | faster-whisper | A separate WER run is required on real audio because released-transcript HiREST bypasses transcription | An end-to-end speech run must transcribe media before applying the same retrieval task | Not wired; it does not block ranking-provider comparison but remains required before an ASR claim | + +Actor clustering is not part of the current LongVALE-derived agent comparison. +Its BBT/Buffy gate remains blocked on lawful access to the source episodes. + +## What the new commands measure + +### VideoPrism + +`msrvtt-action` indexes every video in the declared 1K-A gallery, searches every +caption, reduces multiple VidXP action records to each video's best-ranked +record, and reports text-to-video R@1/5/10/50, median rank, mean rank, and +mAP@10. +Google's released VideoPrism-LvT-B reports MSR-VTT-1K text-to-video R@1, so this +is the correct provider-level comparison. VidXP's multiple fixed records differ +from Google's single global-video evaluation; the result must therefore state +that representation difference instead of claiming exact leaderboard parity. + +`charades-action` searches every action record in the known video and reports +R@1/R@5 at temporal-IoU 0.3/0.5/0.7 plus mean top-one IoU. It tests VidXP's +fixed-window temporal behavior, not VideoPrism's published classification score. + +### Sound + +`finelap-retrieval` retains its command name because it accepts FineLAP's +official five-caption JSONL format. It now evaluates the selected product sound +provider and reports text-to-audio metrics, including R@50. VidXP has no +audio-to-text product operation, so the command does not claim FineLAP's reverse +retrieval score. + +`finelap-grounding` likewise names its TAG-format input, not the active model. +It preserves every labelled occurrence and reports ranked temporal-IoU +diagnostics over PE-A evidence windows. FineLAP's official aggregate uses PSDS +and threshold AUC; this command is an ordering diagnostic and must not be +reported as that official score. + +`finelap-audio-moment` accepts Lighthouse JSONL records for Clotho-Moment or +CASTELLA and runs VidXP's complete current sound search. This is the relevant +product-fit check. A poor result cannot be dismissed by a good short-clip +retrieval score. + +### AEGBench sound-provider comparison + +`aegbench-sound` reads AEGBench `categories` as the sound queries and every +matching `clips` interval as ground truth. It reports threshold-free frame +AUROC, frame average precision, and top-point accuracy separately from interval +metrics. FineLAP interval output uses its calibrated `0.5` threshold; PE-A-Frame +uses its published `0.3` default. No threshold is fitted on the test subset. +Categories present in the manifest without any interval are recorded in +`excluded.json`, not silently scored as misses. + +The frozen selection run sampled 50 of 3,425 manifest rows with +`random.Random(42).sample`, yielding 149 scoreable queries. It is sufficient for +provider selection and runtime comparison, not a full AEGBench leaderboard +claim. The selected PE-A-Frame checkpoint is now the product provider; its +long-audio quality gate remains before the paired agent run. + +### Existing scene and speech adapters + +DiDeMo and HiREST already invoke their pinned official evaluators. Their legacy +full results do not validate the current providers. SigLIP 2 still needs a +declared DiDeMo run, and Qwen3 still needs all 193 HiREST validation pairs. + +## Candidate comparison after the current baseline + +Do not select a replacement from a LongVALE-derived modality slice. Use the +same frozen gates above: + +| Candidate | Run it on | What it can replace if it wins | +| --- | --- | --- | +| [PE-AV](https://huggingface.co/facebook/pe-av-small) | MSR-VTT plus Clotho/AudioCaps, then the temporal product gates | Global VideoPrism retrieval and PE-A frame localization; PE-AV does not supply interval prediction by itself | +| PE-Video or PE-Core video checkpoints | Do not score as text retrieval without an official paired text head | Video encoders, not established drop-in text-video search providers | +| [PE-A-Frame](https://huggingface.co/facebook/pe-a-frame-small) | TAG or [AEGBench](https://arxiv.org/abs/2607.04383), then Clotho-Moment/CASTELLA | Fine-grained sound localization only | +| [AM-DETR](https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/) or another audio moment grounder | Clotho-Moment, real UnAV-100, and CASTELLA when available | The current custom long-audio selector | +| A trained video temporal grounder | Charades-STA plus a second-domain temporal set | The current fixed-window action selector, while preserving VidXP's public action API | + +PE-A-Frame Small was previously tested on four LongVALE-derived slices. One +reference was effectively silent and another accepted only one of several valid +sound occurrences, so that run could not decide provider quality. The later +AEGBench comparison supersedes it for provider selection and selects PE-A-Frame +Small. This still does not validate long-audio product retrieval or PE-AV video +retrieval. + +## Run commands + +The new adapters use supplied datasets and record input checksums, media/model +identities, query text and ground truth, ranked scores and intervals, timings, +and metrics under `benchmark_runs/`. Dataset downloads remain explicit because +the new sources have separate access and licensing terms. + +```bash +vidxp benchmark msrvtt-action \ + --annotations /path/to/MSRVTT_data.json \ + --gallery /path/to/jsfusion_test_ids.txt \ + --media-directory /path/to/msrvtt/videos \ + --run-id current-videoprism + +vidxp benchmark charades-action \ + --annotations /path/to/charades_sta_test.txt \ + --media-directory /path/to/charades/videos \ + --run-id current-videoprism + +vidxp benchmark finelap-retrieval \ + --metadata /path/to/test_metadata_clotho.jsonl \ + --run-id current-finelap + +vidxp benchmark finelap-grounding \ + --metadata /path/to/tag_test.json \ + --audio-directory /path/to/tag/audio \ + --run-id current-finelap + +vidxp benchmark finelap-audio-moment \ + --dataset clotho-moment \ + --metadata /path/to/clotho_moment_test.jsonl \ + --audio-directory /path/to/clotho-moment/audio \ + --run-id current-finelap + +vidxp benchmark aegbench-sound \ + --manifest /path/to/aegbench/manifest.json \ + --audio-directory /path/to/aegbench \ + --provider finelap \ + --run-id current-finelap + +vidxp benchmark aegbench-sound \ + --manifest /path/to/aegbench/manifest.json \ + --audio-directory /path/to/aegbench \ + --provider pe-a-frame \ + --pe-model-directory /path/to/pe-a-frame-small-snapshot \ + --run-id candidate-pe-a-frame +``` + +Use the optional subset-index flags only for execution smokes. A subset is never +reported as a provider-quality result. No component result authorizes the paid +MCP-on/MCP-off run; that still requires explicit maintainer confirmation. diff --git a/docs/benchmarking/model_selection.md b/docs/benchmarking/model_selection.md index 4f339f2c..3df95d8a 100644 --- a/docs/benchmarking/model_selection.md +++ b/docs/benchmarking/model_selection.md @@ -1,142 +1,388 @@ -# Multimodal model and benchmark direction +# Evidence retrieval direction Collection index: [Benchmarking research](README.md) -Status: Current decision record; FineLAP and VideoPrism are implemented, while -other candidate providers remain planned unless architecture says otherwise +Status: Current product and evaluation decision -Last verified: 2026-08-30 +Last verified: 2026-09-07 -## Product requirement +The [research adoption record](research_adoption.md) is the source of truth for +paper-derived product behavior. The [paper inventory](research_papers.md) +records relevant work without implying that VidXP adopts it. -VidXP needs three independently searchable, timestamped evidence channels: +## Product target -1. visual scenes and actions; -2. environmental sounds, music, and other non-speech acoustic events; and -3. spoken words through ASR and text retrieval. +VidXP gives an AI agent a compact, inspectable view of a video library: matching +speech, sounds, frames, action clips, timestamps, and playable evidence. The +agent remains responsible for interpreting that evidence and answering the +user. VidXP does not need to replace the agent with one all-in-one video model. -Query-time fusion must preserve which channel produced each hit. A shared -embedding model is optional; collapsing the channels is not the requirement. -LongVALE makes this boundary explicit because its events can depend on vision, -generic audio, speech, or their temporal relationship. +A product-level comparison succeeds when VidXP matches or improves the agent's +bounded-chunk hit rate while using fewer total tokens. The current benchmark +targets a 10-second evidence clip, accepts 8ā€“12 seconds, and requires at least +half of the event available to one target-size clip. This VidXP serving rule +rejects both blink-length and whole-video answers. Report cached and uncached +input, output, reasoning, time, cost, and calls alongside it. Temporal IoU and +threshold recall remain secondary exact-boundary diagnostics and an explicit +future research limitation. -## Repository baseline +## Providers used in the current agent run -The history before this change contained no shipped CLAP provider or generic-sound -capability. CLAP appears in the later landscape/roadmap research, not in the -application implementation history, so it was not removed by the VideoPrism -change. This branch now implements the missing layer with FineLAP; LAION-CLAP -remains the mature comparison rather than the production provider. +| Lane | Selection | Evidence and limit | +| --- | --- | --- | +| Speech | Keep faster-whisper plus Qwen3 Embedding | The real runtime works; the complete HiREST ranking run and a transcription WER gate remain pending. | +| Scene | Keep SigLIP 2 | The real runtime works; the complete DiDeMo current-provider run remains pending. | +| Action | Keep VideoPrism LvT | It classified all 50 videos in the frozen five-class Kinetics-mini gate correctly through VidXP's current 2 fps/16-frame records. This establishes basic recognition, not temporal localization. | +| Sound localization | Use PE-A-Frame Small; keep FineLAP only as a benchmark control | On the identical 149-query AEGBench subset, PE-A improved frame AUROC from `.8401` to `.8614`, frame average precision from `.7484` to `.7616`, top-point accuracy from `.7315` to `.7651`, and default-threshold mean IoU from `.2924` to `.5226`. It was about 10.2 times slower, but still processed audio 3.35 times faster than playback on `mac-m2-01`. | -VideoPrism is different: it is a current, separately registered temporal-video -capability using `google/videoprism-lvt-base-f16r288` through Transformers. The -new model direction keeps that implementation as the incumbent control while -testing whether Qwen3-VL-Embedding improves text-to-video scene/action retrieval. +This selects providers; it is not a full product score. The isolated agent +pilot has now run with this stack. Replacing VideoPrism with another global +clip-similarity model alone would not fix temporal localization. PE-AV has no +interval head, uses a 3.39 GB checkpoint, and its one-video direct-forward +smoke took 13.36 seconds versus VideoPrism's 7.81-second mean over the 50-video +gate. -## How models are selected +## What the product can claim now -Published benchmark tables, release history, licensing, adoption, artifact -format, and runtime size are sufficient to choose the first integration -candidates. VidXP does not need to spend model or agent runs recreating public -leaderboards before implementation. +- The intended answer is a ranked list of useful, playable evidence chunks, + normally about ten seconds each. It is not a promise to cut the event at its + exact first and last frame. +- PE-A-Frame is the integrated sound-localization provider. On the frozen + subset, it put its highest-scoring 40 ms frame inside a labelled event for + `76.5%` of queries and reached `.523` mean IoU at its released threshold. +- VideoPrism remains the action provider. Its perfect result on five easy + Kinetics classes shows that the model and VidXP preprocessing recognize broad + actions; it does not show that long-video moments are ranked or trimmed well. +- On the selected nine-task pilot, the VidXP agent returned a qualifying clip + on `15/27` repeated runs (`55.6%`), while its visible MCP top three contained + one on `19/27` (`70.4%`). Direct local inspection scored `18/27` (`66.7%`). + These are pilot rates over nine repeated tasks, not general product accuracy. +- The same pilot measured 20.6% fewer agent tokens, 18.3% lower latency, and a + 25.9% lower Promptfoo comparison-cost estimate for VidXP than direct local + inspection. VidXP won 20/27 matched token comparisons and 19/27 latency and + cost comparisons. This establishes an average efficiency gain under the + fixed protocol, not an API bill or an accuracy win. -Local evaluation has a narrower purpose: verify preprocessing, timestamps, -memory, latency, index size, failure behavior, and regressions in this repository. -It does not substitute a tiny private sample for broad published comparisons. -Promptfoo is therefore not required for component-model selection. It is the -selected runner for the separate [Codex MCP-on/MCP-off agent -ablation](agent_ablation.md), where paired task execution, repetitions, traces, -and usage accounting are part of the question. VidXP's Python benchmark code -continues to own dataset preparation and deterministic temporal scoring. +On this CPU-only Mac, PE-A processed 613.43 seconds of audio in about 183 +seconds, so a linear inference-only estimate is roughly 18 minutes per hour of +audio. VideoPrism averaged 7.81 seconds per ten-second Kinetics clip, or roughly +47 minutes per hour at the same sampling policy. These are lane estimates, not +an end-to-end indexing promise; decoding, speech, scene indexing, storage, and +long-video chunk overlap still need an hour-video run. -## Current provider direction +## Current product path -| Role | First direction | Control or ceiling | Reason | -| --- | --- | --- | --- | -| Speech transcription and semantic search | Keep faster-whisper plus the current Qwen3 text-embedding path | Existing released-ASR benchmark paths | Speech and acoustic-event retrieval are different tasks; MAEB shows that no single audio encoder dominates linguistic and environmental-sound work. | -| Environmental-sound retrieval | [FineLAP](https://github.com/xiquan-li/FineLAP), now integrated | [LAION-CLAP](https://github.com/LAION-AI/CLAP) as the mature native-Transformers baseline | FineLAP combines global audio-text retrieval with dense frame features and leads the checked same-table AudioCaps comparison. VidXP supplies fixed ten-second windowing and timestamped dense records. | -| Open-vocabulary sound localization | FineLAP dense features, now stored; compare [PE-A-Frame](https://github.com/facebookresearch/perception_models) | AEGBench methods as research ceilings | Clip retrieval alone cannot identify exact sound intervals, especially repeated or overlapping events. FineLAP integration does not establish boundary quality until AEGBench or LongVALE is run. | -| Visual scene/action retrieval | Evaluate [Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) as the practical candidate | Qwen3-VL-Embedding-8B as the quality ceiling; VideoPrism as the incumbent control | MVEB's text-video table ranks Qwen 8B and 2B first and second. The checked table has no directly comparable VideoPrism row, so this is stronger current selection evidence, not proof that VideoPrism lost a head-to-head. | -| Visual temporal grounding | Evaluate [TimeLens2-4B](https://github.com/MCG-NJU/TimeLens2) after candidate retrieval | TimeLens2-8B and existing temporal baselines | The published 4B average nearly matches 8B at much lower cost. TimeLens2 is visual-only and cannot replace the sound or speech channels. | -| Cross-modal fusion | Keep modality-specific providers and fuse timestamped candidates | A unified permissive audio-video-text encoder can be a later comparison | Separate providers preserve provenance, allow independent upgrades, and match the evidence that different model families lead different modalities and tasks. | -| Query planning and answer synthesis | [Qwen3.5 4B](https://huggingface.co/Qwen/Qwen3.5-4B) through official Ollama `qwen3.5:4b-q4_K_M` | Qwen3.5 9B as a higher-memory comparison | The 4B model has strong published instruction-following and agent results while its official Q4_K_M artifact is approximately 3.4 GB, about half the 9B artifact. VidXP needs bounded schema generation over retrieved evidence, not a second retrieval encoder. | -| Future media evidence enrichment | Reuse Qwen3.5 vision for selected keyframes before adding another model | Evaluate an audio-video model only for top uncitable sound/action hits | The current adapter sends JSON evidence, so multimodal model support alone changes nothing. Media inputs must remain timestamp-bound derived evidence and must not replace FineLAP, scene, action, or speech retrieval. | +VidXP builds reusable local indexes for separate evidence types: -Before promotion, every new checkpoint still needs an immutable revision, artifact -hash, license review, safe-loading review, dependency fit, and a bounded real-media -smoke test. +- faster-whisper and Qwen3 Embedding produce timestamped speech evidence; +- PE-A-Frame Small produces frame-ranked environmental-sound evidence; +- SigLIP 2 retrieves sampled visual frames; +- VideoPrism ranks fixed multi-frame clips by global text-video similarity; and +- reciprocal rank fusion ranks bounded candidates. Each candidate keeps one + anchor hit and at most one directly overlapping hit from each other modality. -## Published selection evidence +This modular path remains the product control. No current evidence requires +replacing every provider or moving to a single trained temporal model. -Scores are comparable only within the named paper and task. +VideoPrism's published action results do not validate this fixed-window +ranking as temporal action localization. A direct conformance check found that +the pinned Transformers port matches Google's official Flax checkpoint; the +remaining action failure is therefore in the product's global-similarity +ranking design, not the converted model weights. -| Source and task | Relevant result | Decision use | -| --- | --- | --- | -| [FineLAP, AudioCaps retrieval](https://aclanthology.org/2026.acl-long.473/) | FineLAP Tā†’A/Aā†’T R@1: 45.7/62.5; the paper's LAION-CLAP row: 35.1/44.2 | Select FineLAP for the first sound integration and retain CLAP as the mature control. | -| [MVEB text-video leaderboard](https://arxiv.org/abs/2606.14958) | Qwen3-VL-Embedding-8B: 60.9 mean; 2B: 58.1; LCO-Embedding-Omni-7B: 56.8 | Prefer Qwen 2B for the practical visual candidate and 8B only when maximizing published quality. | -| [TimeLens2 visual grounding](https://github.com/MCG-NJU/TimeLens2) | Seven-dataset average mIoU: 47.7 for 4B and 48.0 for 8B | Start with 4B; the 0.3-point gain does not justify making 8B the default candidate. | -| [AEGBench](https://arxiv.org/abs/2607.04383) | PE-A-Frame Large: 0.389 mIoU, 0.407 event-F1, 0.607 segment-F1 in the checked table | Use a released specialist to test exact open-vocabulary sound intervals. | -| [Qwen3.5 4B model card](https://huggingface.co/Qwen/Qwen3.5-4B) | Vendor-reported MMLU-Pro 79.1, IFEval 89.8, BFCL-V4 50.3, and TAU2-Bench 79.9; native 262,144-token context | Select the first local planner/synthesizer from published quality evidence; validate only schema retention, grounding, resource use, and failure behavior in VidXP. | -| [Official Ollama Q4_K_M artifact](https://ollama.com/library/qwen3.5:4b-q4_K_M) | 4.66B parameters, Q4_K_M, approximately 3.4 GB, Apache-2.0 | Use the official cross-platform build and an explicit pull instead of bundling weights or relying on a community conversion. | +VidXP already has an optional local SLM path: `query_video` can use the +self-hosted Ollama `qwen3.5:4b-q4_K_M` model for typed query planning and +grounded answer synthesis, with deterministic evidence fallback. This is a +VidXP product option, not a retroactive replacement for the Codex MCP +condition. Evaluate it as a separate local-answer lane. -VideoPrism remains a credible multi-frame video encoder. The decision above does -not reject it on quality. It rejects two unsupported claims: that implementation -friction still blocks it, and that it is automatically the first text-video -retrieval pick despite being absent from the current common MVEB comparison. +The paper-facing SLM condition is narrower and distinct: the custom Python +provider gives the same managed model only the event text plus a concise system +definition of the four indexed modalities. Promptfoo also supplies full test +rows to custom providers. The adapter selects only the public query inputs and +does not read the correct time range or expected modalities, keeping the answer +key away from the model and retrieval path. The model makes one typed routing +decision. The harness then runs the fixed MCP retrieval lifecycle and copies +VidXP's top three ready evidence +tiles without model inspection or reranking. The same output schema and +deterministic scorers used for Codex apply. This is a router-assisted retrieval +test, not a skill-, tool-discovery, evidence-synthesis, or general-agent test. +The general Codex skill stays out of this lane. The provider reports the routing +tokens, one model request, selected modalities, MCP calls, and total latency. +Memory, energy, and local compute cost remain unmeasured rather than being +treated as zero. This tests whether VidXP can serve a local agent without +external model exposure; it does not claim that the harness agent is already a +shipped VidXP UI feature or that local inference is costless. -## What each dataset or benchmark contributes +The generation settings come from two declared sources. The Qwen Team's +[ā€œQwen3.5: Towards Native Multimodal Agentsā€ model card](https://huggingface.co/Qwen/Qwen3.5-4B) +recommends a 32,768-token output allowance and temperature `0.7`, top-p `0.8`, +and presence penalty `1.5` for ordinary non-thinking requests. Ollama's +[context guidance](https://docs.ollama.com/context-length) recommends at least +64,000 tokens for agents and tool use. VidXP adopts those values for the +managed runtime and disables thinking. The 64,000 value is an Ollama runtime +recommendation, not a Qwen research result; Qwen reports a 262,144 native +context but also warns that memory may require a smaller allocation. On this +8 GB evaluation Mac, VidXP combines the 64,000 allocation with Ollama's +documented Flash Attention and 8-bit KV-cache settings. The old 1,024-token +product and 2,048-token benchmark ceilings had no cited model basis and are +removed. -| Dataset or benchmark | Use in VidXP | Does not establish | -| --- | --- | --- | -| [MAEB](https://arxiv.org/abs/2602.16008) | Broad audio-embedding selection across speech, music, environmental sound, and audio-text tasks | Long-video timestamp accuracy or end-to-end VidXP quality | -| [MVEB](https://arxiv.org/abs/2606.14958) | Common video-embedding selection across retrieval and other representation tasks, including paired video-only and audio-plus-video variants | A direct VideoPrism comparison, unrestricted temporal localization, or system latency | -| [AEGBench](https://arxiv.org/abs/2607.04383) | Open-vocabulary environmental-sound interval grounding, including difficult and repeated events | Visual or speech retrieval | -| [LongVALE](https://github.com/ttgeng233/LongVALE) | Primary combined target: Omni-TVG for vision, sound, and speech event localization in long videos | Actor clustering; its captioning tasks are relevant only if VidXP claims generation | -| [FLARE](https://flarebench.github.io/) | Secondary long-video retrieval stress test with visual-only, audio-only, and hard joint queries | Human-authored-query generalization; the queries are model-generated and filtered | -| [OVSD](https://research.ibm.com/publications/robust-and-efficient-video-scene-detection-using-optimal-sequential-grouping) | Open-licensed scene-boundary segmentation data and a useful temporal-unit regression set | Natural-language retrieval, action recognition, environmental-sound search, speech search, or cross-modal fusion | -| [MultiVENT 2.0](https://huggingface.co/datasets/hltcoe/MultiVENT2.0) | Large-corpus event retrieval for visual, ASR, OCR, and metadata channels | Generic acoustic-event retrieval or moment boundaries | - -LongVALE supplies three tasks: omni-modal temporal grounding, dense video -captioning, and segment captioning. Omni-TVG directly matches VidXP's search and -timestamp contract. The two captioning tasks should not be adopted merely because -they share the dataset. - -## Remaining benchmark gap - -A generic centralized audio or video embedding leaderboard is not new white -space: MAEB and MVEB already provide that infrastructure in the MTEB ecosystem, -and AEGBench, LongVALE, and FLARE cover adjacent temporal and multimodal slices. - -The defensible gap is narrower: a live, reproducible long-video system benchmark -that combines scene/action, environmental-sound, and speech queries; scores both -retrieval and exact boundaries; includes modality-isolation and fusion ablations; -uses realistic queries; and reports latency, memory, index size, and -commodity-hardware behavior. If VidXP publishes this, it should extend or -interoperate with the MTEB/MOEB ecosystem instead of creating an isolated model -leaderboard. - -## Cost and execution policy - -Reading published papers, leaderboards, model cards, and open benchmark metadata -does not consume Codex, Claude, or model-inference runs. Downloading and running -open checkpoints locally normally has no per-call API charge, but it does consume -the machine's storage, memory, electricity, and time; dataset and checkpoint -licenses can also restrict use. - -Metered model or agent comparisons are not part of the selection gate. Spend -local compute only after the provider exists, using the smallest smoke that can -catch integration defects. Schedule full MAEB, MVEB, LongVALE, FLARE, or AEGBench -runs only when their result answers an approved paper or release question. - -## Implementation order - -1. Validate the implemented FineLAP sound capability on a bounded real-media - sample, then run the LongVALE one-archive adapter pilot. -2. Compare LAION-CLAP as the mature integration baseline and PE-A-Frame where - boundary quality requires a specialist. -3. Add or replace the visual video-embedding provider with - Qwen3-VL-Embedding-2B while keeping current and VideoPrism controls. -4. Add TimeLens2-4B only after cheap candidate retrieval, for visual temporal - proposal or reranking work. -5. Run LongVALE Omni-TVG and FLARE with all three evidence channels and frozen - fusion. Keep OVSD as a scene-boundary component test. +For the Promptfoo lane, Pydantic AI's +[native structured-output mode](https://ai.pydantic.dev/output/#native-output) +constrains the one model response to a non-empty, unique subset of `scene`, +`action`, `sound`, and `speech`. The harnessā€”not the modelā€”resolves the dataset +filename through `list_media`, submits the unchanged query, waits for the job, +and retrieves its evidence board. This avoids spending local-model time and +tokens copying data that VidXP already returned in a typed form. + +## Confirmed limits and decisions + +### Keep FineLAP as a historical benchmark control + +Xiquan Li et al., [ā€œFineLAP: Taming Heterogeneous Supervision for Fine-grained +Language-Audio Pretrainingā€](https://aclanthology.org/2026.acl-long.473/), ACL +2026, Sections 3.2ā€“3.3, trains separate global and local audio projections for +clip-level and frame-level supervision. VidXP previously stored both outputs in +one collection and ranked the raw records together. + +That integration was invalid: the two score lists did not form one calibrated +ranking. On four held-out sound tasks, the mixed top three contained target +evidence on 0/4 tasks; querying the representations separately did so on 3/4. + +FineLAP supports separating the global and local outputs. It does not establish +VidXP's global top-three gate followed by one pooled activation ranking over +those windows. Section 3.3 trains local scores against short event phrases and +frame labels inside a clip; the paper's Limitations section explicitly leaves +long-form audio and temporally enhanced audio-text retrieval unevaluated. The +VidXP selector returned no final top-three overlap against the four designated +intervals and missed the two unambiguous cases. The four-task rate is not a +valid provider score because one reference is silent and another query has +multiple correct occurrences. Treat the selector as historical and unvalidated, +not adopted or conclusively rejected. It remains reproducible in the benchmark +adapter. Product index schema 8 replaces both representations with PE-A frames, +so older indexes must be rebuilt. + +The sound provider must localize a free-form acoustic description, including +short environmental events, and return every useful occurrence. It does not +need to interpret visual or speech-only clauses; those belong to the other +providers and the agent. Two research tasks are therefore relevant: + +- audio moment retrieval tests sentence-to-interval retrieval over minutes of + audio; and +- open-vocabulary sound-event grounding tests fine event boundaries and + repeated or overlapping occurrences. + +These are sound-provider diagnostics, not substitutes for LongVALE's combined +task. Product search passes the same full query to each requested modality and +applies reciprocal rank fusion. A hit seeds a bounded candidate and can receive +support only from the best directly overlapping hit in each other modality. +Indirect overlap cannot join distant moments, and hits from the same modality +remain separate candidates. A sound result can therefore support a visual +match without merging with another sound match elsewhere in the video. + +The API now separates candidate collection from final output. `top_k` limits +only the fused results returned to the caller. `candidate_top_k` independently +limits each modality to 100 hits by default; MCP evidence delivery then shows +three fused candidates by default. Cormack, Clarke, and Buettcher's RRF paper +supports the rank formula and its `k = 60` constant. It does not prescribe +either output limit. The candidate budget is a VidXP resource cap: 100 matched +exhaustive input on the corrected ten-task control, but is not a general +accuracy optimum. + +Fresh fused queries use the `rrf_v2` identity. Existing indexes remain valid, +and stored `connected_intervals` provenance remains readable. + +The original saved-ranking depth control confirmed that candidate depth could +not be selected while transitive overlap corrupted the output. At full depth, +every top result covered nearly its entire video. After direct-overlap fusion +replaced that grouping, depths 100 through all produced identical metrics +instead of collapsing. The corrected run still reached only `0.20` R@5 at +tIoU 0.5, so it fixes candidate identity but not provider ranking or boundary +errors. Neither curve selects a serving depth. + +The model papers keep this seam simpler than the current implementation. +FineLAP exposes separate clip- and frame-level representations; VideoPrism is a +frozen video encoder; and SigLIP 2 is an image-text encoder whose localization +results use downstream heads. None defines temporal rank fusion. LongVALE +Section 3.2 first builds semantically coherent visual and audio events, then +combines those event boundaries while preserving audio integrity. VidXP's +direct-overlap rule prevents false video-length unions, but model-specific +event proposals remain the next boundary-quality seam. + +[DCASE 2026 Task 6](https://dcase.community/challenge2026/task-audio-moment-retrieval-from-long-audio-results) +is the strongest direct long-audio evidence found. Its official +MS-CLAP/QD-DETR baseline scored 13.56 R1@0.7; a 211.87M-parameter +M2D-CLAP/CG-DETR entry reached 48.59, but public code and weights for that entry +were not verified. The released CASTELLA/Lighthouse control reached only 20.3 +R1@0.7, is weak on sub-ten-second moments, truncates audio-feature sequences +beyond 300 seconds, and conflicts with the managed runtime. A separate runtime +would reproduce that baseline; it has no demonstrated product advantage. + +The first executable dense-sound candidate tested was Meta's +[PE-A-Frame Small](https://huggingface.co/facebook/pe-a-frame-small), from Vyas +et al., [ā€œPushing the Frontier of Audiovisual Perception with Large-Scale +Multimodal Correspondence Learningā€](https://arxiv.org/abs/2512.19687). It +accepts free-form audio descriptions and emits frame scores and multiple spans +at about 40 ms resolution. The Apache-2.0 checkpoint has 450M parameters and a +1,758,756,416-byte F32 weight file. Its official localization AUROC is +0.83ā€“0.96 across the published event-localization sets; AUROC is not interval +IoU and does not establish VidXP accuracy. The installed Transformers runtime +has the official PE-Audio classes, avoiding the source repository's optional +`xformers` path. + +The pinned Small checkpoint failed the initial flawed Mac product diagnostic. A +complete 73.14-second soundtrack took 244.35 seconds on CPU and peaked at 4.30 +GiB RSS. The full query +missed the phone-ring target and produced 125 fragments at the official 0.3 +threshold. On target-aware clips, which test recognition but not retrieval, the +mean best-span IoU was 0.1654 for full queries and 0.1151 for sound-only phrases; +the target outscored surrounding audio on only one of four full-query cases and +none of the sound-only cases. Threshold tuning cannot fix a target whose score +is below the surrounding audio. PE-A-Frame Small was therefore not adopted from +that run. The diagnostic was not a native provider benchmark: two of its four +labels were unsuitable for sound-only scoring. + +The subsequent frozen AEGBench comparison supplied the missing valid gate. It +used 50 recordings sampled with seed 42 from the 3,425-row manifest, 149 +categories with annotated intervals, and every repeated interval. Two manifest +categories with no interval were excluded explicitly. PE-A-Frame Small beat +FineLAP on every ranking and default-threshold interval measure in the selection +table while remaining faster than playback on the CPU-only Mac. This selects +PE-A-Frame Small for sound localization. It does not select PE-AV for +action/video, and it is not a full AEGBench leaderboard result. + +For long media, VidXP defaults to ten-second inference sections with a +two-second overlap, assigns each overlap at its midpoint, and maps the retained +40 ms frames to global timestamps. Search ranks with the checkpoint's dot +product and returns the best frame per fixed ten-second evidence window. The +values are configurable deployment defaults, not PE-A-Frame claims or measured +accuracy optima. Keep distinct repeated events separate. + +### Treat fused intervals as bounded evidence candidates + +The current fusion anchors each candidate to one ranked hit. It adds at most +the best directly overlapping hit from each other modality and returns the +smallest interval containing that evidence. It never merges same-modality hits +or follows an overlap chain into another moment. The RRF formula and `k = 60` +come from Gordon Cormack, Charles Clarke, and Stefan Buettcher, +[ā€œReciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning +Methodsā€](https://doi.org/10.1145/1571941.1572114), SIGIR 2009. Candidate +construction and interval boundaries remain VidXP engineering. + +The direct-overlap correction removes video-length chains, but its ten-task +replay reached only `0.20` R@5 at tIoU 0.5. Several correct action regions +remain eight-second windows around two-second references, and some target +evidence is ranked far below five by its provider. Candidate construction no +longer corrupts separate moments, but precise boundaries and ordering still +depend on the modality providers. + +No replacement boundary model has been selected. The overlapping-action +control did produce a near-target shorter record, but the then-existing union +joined it to its neighbors. A held-out follow-up then tested a simple +coarse-to-fine path without union. Fine candidate recall improved, but the +coarse gate and similarity ranking missed most answers, so that path is not a +product fix. +Point-to-Span and shot-proposal fusion also remain concluded benchmark controls. +Their exact results and deviations are recorded in the +[research adoption record](research_adoption.md). + +## Next actions + +### Sound + +FlexSED's pinned released path was also tested. It processed +616.7 seconds of unique audio in 10.85 seconds and peaked at 1.57 GiB RSS, so +the runtime fits. Quality did not: target audio outscored the rest of its +soundtrack on 0/4 full queries and 0/4 sound-only phrases. At the published +0.5 threshold with a nine-frame median, only the engine case overlapped its +reference, at about 0.045 IoU. Overlap cannot repair raw target scores below +unrelated regions. It also missed the two unambiguous audible targets, siren +and drumbeat. Do not select it from this result, but do not report `0/4` as a +valid provider-quality estimate: the phone reference is invalid and the engine +query has multiple correct occurrences. + +The reference-audio check found one invalid component case. The annotated +telephone-ring interval has `-91.75 dBFS` RMS and `-78.27 dBFS` peak signal; +the preceding five seconds are `-45.85 dBFS`. The local MP4 is byte-identical +to the downloaded LongVALE archive, so this is not local corruption. Quarantine +that task from sound-only scoring pending human review; do not silently remove +it from the multimodal pilot. + +The engine task exposes a separate protocol error. Its sound-only phrase can +correctly match several engine-rev occurrences. WSTAG's top frame at 242.22 +seconds falls inside LongVALE's separate 241.760ā€“243.554-second annotation for +the Cayenne engine rumbling and revving. The scorer nevertheless marks it wrong +because it accepts only 25.560ā€“27.560 seconds, where the multimodal query also +specifies a gesturing driver. A sound provider cannot use that visual clause. +Sound-component evaluation must label every acoustically matching occurrence; +the existing single reference remains valid only for the full multimodal +fusion task. + +DASM is not an executable Mac candidate. The official text-query notebook at +Transformer4SED revision `c3e883d0fbeaf7031b467d45a3c46a88a76c00b6` +hard-codes CUDA and a local checkout, and requires a separate MGA-CLAP +repository and checkpoint. Its model hub publishes 636 MB of DASM artifacts +under MIT metadata, but the source repository contains no software license. +Do not copy, port, or benchmark that implementation unless the authors clarify +the code license and provide a supported non-CUDA path. + +Xu et al., [ā€œTowards Weakly Supervised Text-to-Audio +Groundingā€](https://arxiv.org/abs/2401.02584), IEEE Transactions on Multimedia +2024, provides the next lawful CPU path. The authors recommend a newer +AudioCaps-v2/LAION-CLAP Hugging Face model rather than the paper's original +checkpoint. Against the current single-reference control, neither the full +query nor the sound phrase ranked the designated target first on the three +audible tasks. +Mean target-best frame percentile was 0.8688 and 0.8985 respectively, but the +official `0.5` inference threshold returned no target-overlapping interval, so +IoU was zero on all six passes. Six CPU forwards over 1,679.9 seconds of input +audio took 25.82 seconds; each 247ā€“296-second recording took 3.42ā€“5.02 seconds, +and peak process RSS was 4.15 GiB. WSTAG missed both unambiguous cases at that +threshold; the engine top result was a separate valid occurrence. It is not +selected, but the flawed three-task control cannot provide a final quality +estimate. Its hub metadata is +also missing the `AutoModel` mapping advertised by its README; the local test +loaded the same published class and exact weights directly with zero checkpoint +mismatches. + +Three stronger-looking releases do not satisfy the product gate: + +- Wu et al., [FLAM](https://arxiv.org/abs/2505.05335), ICML 2025, is the closest + compact technical fit, but OpenFLAM is non-commercial and its public model is + not the internal model used for the paper's reported results. +- Sun et al., [SpotSound](https://arxiv.org/abs/2604.13023), ACM MM 2026, directly + trains short-event timestamp grounding, but it is a LoRA over the 8B + Audio Flamingo 3 base, whose license is non-commercial and whose supported + runtime is Linux/CUDA. +- Wang et al., [TimeAudio](https://arxiv.org/abs/2511.11039), 2025, uses a + Vicuna-7B stack and documents more than 40 GB of GPU memory for inference. + +Those candidates supplied no better distributable Mac path. The later valid +AEGBench comparison selected PE-A-Frame Small, which is now integrated. FineLAP +remains only as the recorded comparison control. Do not build a separate +DCASE/Lighthouse runtime unless a reproducibility comparison is explicitly +needed. + +### Action + +Do not tune fusion, window overlap, or query wording again for this failure. +The held-out comparison already showed that useful fine windows exist but raw +VideoPrism similarity ranks most of them too low. + +The replacement boundary is now explicit: keep VidXP's action API and reusable +index, but replace global clip ranking with a trained temporal grounder that +consumes a sequence of visual features and predicts intervals. An et al., +[HieraMamba](https://openaccess.thecvf.com/content/CVPR2026/html/An_HieraMamba_Video_Temporal_Grounding_via_Hierarchical_Anchor-Mamba_Pooling_CVPR_2026_paper.html), +CVPR 2026, establishes the long-video multi-scale grounding design. An, Jain, +and Grauman, [UniversalVTG](https://arxiv.org/abs/2604.08522), 2026, adds one +cross-domain checkpoint and is the closest technical product candidate. + +Neither release can be adopted unchanged: both depend on a CUDA-oriented Mamba +stack, and the checked repositories do not provide a top-level product license. +The next implementation task is therefore a bounded compatibility decision: +confirm a lawful checkpoint and a CPU or Apple-Silicon runtime for that exact +grounder. If either requirement fails, reject it; Lighthouse's 150-second video +encoder limit is benchmark context, not a fallback for the sound provider. Do +not change product ranking until one candidate passes that gate on the frozen +action tasks. diff --git a/docs/benchmarking/paper_validation.md b/docs/benchmarking/paper_validation.md index c068f2b3..0334e381 100644 --- a/docs/benchmarking/paper_validation.md +++ b/docs/benchmarking/paper_validation.md @@ -18,15 +18,28 @@ actually evaluates, not what its title or abstract appears to imply. marked as context, custom evaluation, or artifact-blocked rather than being silently treated as executable comparisons. -The inventory contained 57 paper rows when this validation pass began. The audit -first added 18 omitted benchmark-defining, protocol-lineage, and direct-comparator -papers, then the published-results pass added three directly relevant retrieval -comparators that the first audit missed: MMMORRF, OmniEmbed-MultiVENT, and Q2E. -The 2026-08-27 model-selection refresh added MAEB, MVEB, FineLAP, Auto-AEG/ -AEGBench, TimeLens2, and the OVSD-defining paper. The reconciled inventory now -contains 85 unique paper rows. Every inventory paper's -exact source URL appears in a -paper-level ledger row below; the final coverage check found zero omissions. +The inventory began with 57 paper rows. Later passes added omitted benchmark +definitions, comparator papers, current component-model work, and the temporal +representation/grounding lineage. Every inventory paper's exact source URL must +also appear in a paper-level ledger row below; keep that coverage check current +instead of relying on a historical row count. + +## Literature-coverage anchors + +| Source | Evidence checked | Coverage used here | Limit | +| --- | --- | --- | --- | +| [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://doi.org/10.1109/TPAMI.2023.3258628) | Full text, taxonomy, method comparisons, benchmark discussion, and search procedure checked | Common pipeline plus proposal-based, proposal-free, reinforcement-learning, and weakly supervised families through September 2022 | A survey boundary, not evidence for post-2022 completeness or product adoption | +| [A Survey on Temporal Sentence Grounding in Videos](https://doi.org/10.1145/3532626) | Full text, taxonomy, datasets, metrics, and future directions checked | Independent pre-2022 coverage check, including the role of audio and weaknesses in existing evaluation | Does not cover the rapid frozen-VLM, video-LLM, or 2023ā€“2026 model lineages | + +Post-2022 coverage was extended through the cited primary papers, their related- +work lineages, and official artifacts. The inventory is scoped by product +relevance; it is not represented as an exhaustive bibliography of the field. + +## Adopted method provenance + +| Paper | Evidence checked | Published method | VidXP adoption boundary | +| --- | --- | --- | --- | +| [Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods](https://doi.org/10.1145/1571941.1572114) | Full two-page paper and current implementation checked | Sums `1 / (k + rank)` across ranked lists; `k = 60` was fixed after a pilot and retained for validation | VidXP adopts the formula and constant. Grouping overlapping temporal hits, taking one best rank per modality, and returning the component union are VidXP-specific and unsupported by this paper. | ## Current component-model selection @@ -34,16 +47,51 @@ paper-level ledger row below; the final coverage check found zero omissions. | --- | --- | --- | --- | --- | | [MAEB](https://arxiv.org/abs/2602.16008) | Full text and released MTEB relationship checked | Thirty representative audio-embedding tasks selected from a 98-task pool; 50+ models across speech, music, environmental sound, and cross-modal audio-text work | Task-family metrics and aggregate/Borda comparisons | Correct broad source for audio-provider selection. It shows that speech-pretrained and contrastive audio-text models lead different domains; it does not measure long-video windowing or VidXP. | | [MVEB](https://arxiv.org/abs/2606.14958) | Full text and main/appendix result tables checked | Twenty-three representative video-embedding tasks selected from a 184-task pool; 33 models; paired video-only and audio-plus-video variants plus modality-restricted tables | Classification, clustering, retrieval, QA, and aggregate means; text-video Table 11 | Qwen3-VL-Embedding-8B/2B rank first/second on the checked text-video table at 60.9/58.1. VideoPrism is absent, so no direct quality claim between them is valid. | -| [FineLAP](https://aclanthology.org/2026.acl-long.473/) | Full text, official repository, and checkpoint surface checked | AudioCaps/Clotho retrieval, classification, sound-event detection, and text-to-audio grounding | Retrieval R@1 plus task-specific dense metrics | Supports FineLAP as the first sound candidate: AudioCaps Tā†’A/Aā†’T R@1 45.7/62.5 versus the paper's LAION-CLAP 35.1/44.2. Fixed ten-second input remains a VidXP integration constraint. | +| [FineLAP](https://aclanthology.org/2026.acl-long.473/) | Full text, official repository, and checkpoint surface checked | AudioCaps/Clotho retrieval, classification, sound-event detection, and text-to-audio grounding | Retrieval R@1 plus task-specific dense metrics | Supports clip retrieval and frame/event-phrase scoring. Its stated lack of variable-length and long-form modeling means it does not support VidXP's global-window gate or final long-audio interval ranking. | +| [Language-based Audio Moment Retrieval](https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/) | Full text, project page, release links, and documented Lighthouse execution path checked | Clotho-Moment, a manually annotated 100-query UnAV-100 subset, and TUT Sound Events 2017 | R1 at tIoU 0.5/0.7 and mAP | Direct trained long-audio comparator. AM-DETR processes one-second-hop clip features with cross-modal and temporal attention; on UnAV-100 it improved R1@0.7 by 9 points over a validation-tuned sliding-window baseline. | +| [CASTELLA](https://arxiv.org/abs/2511.15131) | Full text, official dataset repository, Lighthouse release, and checkpoint record checked | 1,862 real recordings lasting 60ā€“300 seconds; 3,881 free-form captions and 11,308 intervals | R1 at tIoU 0.5/0.7 and mAP | Direct product-fit benchmark. Clotho-Moment pretraining plus CASTELLA fine-tuning reached R1@0.7 20.3 with UVCOM; the paper also reports weak performance for moments shorter than ten seconds. | +| [DCASE 2026 Task 6](https://dcase.community/challenge2026/task-audio-moment-retrieval-from-long-audio-results) | Official task, evaluator contract, final leaderboard, system metadata, and technical-report abstracts checked | Natural-language interval retrieval over 1ā€“5 minute audio; hidden evaluation has 100 recordings and 177 queries | Primary R1@0.7, plus R1@0.5 and mAP | Strongest direct evidence found. The official MS-CLAP/QD-DETR baseline scored 13.56 R1@0.7; an M2D-CLAP/CG-DETR entry reached 48.59 with 211.87M total parameters. No public code or weights for that winning entry were verified. | +| [PE-AV / PE-A-Frame](https://arxiv.org/abs/2512.19687) | Paper, Apache-2.0 model card, official source, exact Small checkpoint, installed Transformers API, and local Mac diagnostic checked | Free-form audio event localization on Internal, ASFX-SED, AudioSet Strong, DESED, and UrbanSED | Frame-localization AUROC | Small reports 0.83ā€“0.96 AUROC and stays close to Base/Large while using 450M parameters. The pinned F32 weight file is 1,758,756,416 bytes. Local LongVALE-derived results did not transfer: a 73.14-second full-track query missed its target in 244.35 seconds, and four target-aware clips also ranked surrounding audio above the target in most cases. | +| [Detect Any Sound](https://arxiv.org/abs/2507.16343) | Full paper claims, project page, official Transformer4SED revision `c3e883d0fbeaf7031b467d45a3c46a88a76c00b6`, inference notebook, dependency instructions, and model-hub tree checked | Open-vocabulary SED on AudioSet Strong and zero-shot DESED | PSDS and frame/event detection measures | Relevant event-phrase detector, but not an executable VidXP candidate: released text inference hard-codes CUDA, requires separate MGA-CLAP code and weights, and the official source repository has no software license. The 636 MB hub artifact being marked MIT does not license that source code. No local quality score was produced. | +| [FlexSED](https://github.com/JHU-LCAP/FlexSED) | Paper, MIT repository, inference API, exact pretrained checkpoint, pinned LAION CLAP, and local Mac run checked | Open-vocabulary SED on AudioSet Strong | PSDS1 and classwise zero-/few-shot comparisons | Runtime is viable: 616.7 seconds of audio ran in 10.85 seconds at 1.57 GiB peak RSS. It missed both unique valid pilot events. The original `0/4` target-only result cannot be treated as a provider score because one event is silent and another query has several correct occurrences. | +| [WSTAG](https://arxiv.org/abs/2401.02584) | IEEE Transactions on Multimedia 2024 paper, MIT repository revision `40c2280139a9bd077a6c823319d6244f5aa7512d`, inference rule, and author-recommended AudioCaps-v2 model checked | Weakly supervised phrase and sentence grounding from audio-caption data | PSDS, Th-AUC, and thresholded segment metrics | The tested 2025 Hugging Face model is an official post-paper release, not the paper's original checkpoint. It ran quickly on CPU and missed the unique siren and drumbeat cases. Its 242.22-second engine top is another annotated engine rev, exposing the pilot's invalid single-reference scoring. | +| [FLAM](https://arxiv.org/abs/2505.05335) | ICML 2025 paper, project page, OpenFLAM code/model release, examples, and license checked | Open-vocabulary frame-wise event localization plus clip retrieval | Frame-wise SED and audio-text retrieval metrics | Closest compact technical fit found. It is not adoptable as released: OpenFLAM is non-commercial, and its public checkpoint is trained on public data rather than the unavailable internal model used for the paper's main results. | +| [SpotSound](https://arxiv.org/abs/2604.13023) | ACM MM 2026 paper, project, official code, 80.8 MB adapter, and Audio Flamingo 3 base requirements checked | Short-event audio temporal grounding, negative-event rejection, and long-audio grounding | mIoU, presence/absence accuracy, joint F1, and SED metrics | Strong direct research direction, but not a Mac product candidate. The adapter depends on the non-commercial 8B Audio Flamingo 3 base; the official path pins CUDA-era PyTorch and targets Linux/NVIDIA hardware. | +| [TimeAudio](https://arxiv.org/abs/2511.11039) | Paper, model card, dependency list, and inference requirements checked | Temporal grounding, dense captioning, and long-audio understanding | Task-specific temporal and language metrics | Apache-marked release, but its Whisper/BEATs/Vicuna-7B stack documents more than 40 GB GPU memory for inference. It does not fit the managed Mac deployment. | | [Auto-AEG and AEGBench](https://arxiv.org/abs/2607.04383) | Full text, HTML tables, and dataset link checked | Open-vocabulary audio event grounding over 3,427 items/9,790 queries with difficulty-stratified hard cases | mIoU, recall/precision IoU, event F1, segment F1, and onset precision/recall | Direct environmental-sound boundary benchmark. Table 3 reports PE-A-Frame Large at 0.389 mIoU/0.407 event-F1/0.607 segment-F1; the larger trained Auto-AEG system is research ceiling context. | | [TimeLens2](https://github.com/MCG-NJU/TimeLens2) | Official paper/repository and released checkpoint table checked | Seven visual temporal-grounding datasets with 2B/4B/8B checkpoints | Average mIoU and per-dataset temporal-grounding metrics | The official release reports 47.7 average mIoU for 4B and 48.0 for 8B. Select 4B first; all variants are visual-only. | | [OVSD defining paper](https://research.ibm.com/publications/robust-and-efficient-video-scene-detection-using-optimal-sequential-grouping) | Primary IBM publication and later dataset-use records checked | Scene-boundary segmentation over open-licensed movies and animations | Scene-segmentation measures | Useful temporal-unit regression source only. OVSD contains no text-query retrieval, action, environmental-sound, speech, or fusion objective. | +## Temporal representation and grounding evidence + +| Paper or release | Evidence checked | Actual experimental use | Measures/results reported | Validation outcome | +| --- | --- | --- | --- | --- | +| [LGSS](https://openaccess.thecvf.com/content_CVPR_2020/html/Rao_A_Local-to-Global_Approach_to_Multi-Modal_Movie_Scene_Segmentation_CVPR_2020_paper.html) | Full paper and supplementary material checked | MovieScenes: 21,000 scene segments from 150 movies; place, cast, action, and audio shot features | Scene-boundary AP, mIoU, recall, and recall near boundaries | Established evidence for multimodal shot-to-scene segmentation. It predicts scene boundaries, not natural-language moments. | +| [ShotCoL](https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.html) | Full paper checked | MovieNet scene detection and 19,119 ad cue points from 3,975 movies/episodes | Scene-boundary and cue-point measures; label, parameter, and runtime comparisons | Reports competitive scene detection with 25% of labels, nine times fewer parameters, and seven times faster runtime. This supports efficient shot representations, not query retrieval. | +| [BaSSL](https://github.com/kakaobrain/bassl) | Paper, official repository, checkpoint, and environment checked | MovieNet scene segmentation | Scene-boundary AP | Reproducible scene-boundary comparator with code/checkpoint. Its Python 3.7, PyTorch 1.7, and CUDA 11 environment is not a ready macOS product dependency. | +| [NeighborNet](https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Neighbor_Relations_Matter_in_Video_Scene_Detection_CVPR_2024_paper.html) | Full paper and official repository checked | Public video-scene detection datasets | Scene-boundary AP | Reports at least six AP points over released prior methods by adding neighboring-shot context. It remains a segmentation component. | +| [UMT](https://openaccess.thecvf.com/content/CVPR2022/html/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.html) | Full paper, official code, checkpoints, and environment checked | QVHighlights, Charades-STA, YouTube Highlights, and TVSum with aligned visual/audio features where applicable | Moment R@K/tIoU and mAP; highlight mAP/Hit@1 | Established audiovisual query-conditioned interval/highlight comparator. Checkpoints are target-trained and the official environment assumes CUDA 11.5/PyTorch 1.11. | +| [Zero-Shot VMR From Frozen VLMs](https://openaccess.thecvf.com/content/WACV2024/html/Luo_Zero-Shot_Video_Moment_Retrieval_From_Frozen_Vision-Language_Models_WACV_2024_paper.html) | Full method, IID/OOD tables, component and hyperparameter ablations checked; official code not found | Charades-STA, ActivityNet Captions, and TACoS with frozen CLIP/InternVideo, query-conditioned feature refinement, `k`-means proposals, and bottom-up compound-query merging | R@1 at tIoU 0.1/0.3/0.5/0.7 and mIoU, depending on dataset/split | Strict zero-shot and directly relevant to compound queries. `k = 6`, refinement settings, and other choices were selected on Charades-STA, so the method must be reproduced rather than copied piecemeal. | +| [TFVTG](https://arxiv.org/abs/2408.16219) | Full paper, project page, and official repository checked | Charades-STA and ActivityNet Captions under IID, OOD, and cross-dataset settings; LLM decomposition/order plus VLM dynamic/static proposal scoring | R@1 at tIoU thresholds and mIoU | Peer-reviewed, official-code compound-query baseline. It depends on stored LLM outputs or query-time LLM work and proposal enumeration, so it is not evidence for a cheap offline-only replacement. | +| [Anchor-Aware Similarity Cohesion](https://openaccess.thecvf.com/content/CVPR2025/html/Tan_Anchor-Aware_Similarity_Cohesion_in_Target_Frames_Enables_Predicting_Temporal_Moment_CVPR_2025_paper.html) | Full paper, method/ablation tables, and official repository checked | QVHighlights, Charades-STA, and ActivityNet Captions using CLIP features, query-conditioned alignment, and 2D boundary prediction | R1@0.5/0.7 and moment mAP | Strong supervised boundary evidence, not a frozen heuristic. It trains for ten epochs and uses dataset-specific convolution widths, so it is a trained visual ceiling rather than a direct VidXP fusion patch. | +| [Point-to-Span](https://arxiv.org/abs/2512.10363) | Full method, main results, and ablations checked; no official code found | MAD and MomentSeeker with adaptive smoothing/peak expansion, ordered query decomposition, reranking, and missed-span injection | R@1/5 at tIoU 0.1/0.3/0.5 | Direct long-video search-then-refine candidate. It reports strong gains but remains an unreviewed preprint without a checked executable artifact. | +| [GranAlign](https://arxiv.org/abs/2601.00584) | Full method, main results, query-type analysis, and hyperparameter sensitivity checked; no official code found | QVHighlights, Charades-STA, and ActivityNet Captions with dual-granularity query rewriting and query-aware captions | Standard moment R@1/mIoU and QVHighlights mAP | Accepted to AAAI 2026 and useful for semantic-granularity failures. It adds repeated LLM/VLM generation at query time and is not an offline index method. | +| [UniversalVTG](https://arxiv.org/abs/2604.08522) | Full paper claims plus official checkpoint, inference API, feature format, environment, and license notes checked | GoalStep-StepGrounding, Ego4D-NLQ, TACoS, Charades-STA, and ActivityNet Captions under one cross-dataset-trained model | Dataset-specific interval metrics | Lightweight relative to video LLMs and executable from pre-extracted features, but the official evaluation/extraction path requires CUDA, rebuilds 1D NMS, and inherits a separate Meta/Fair encoder license. | +| [REZE](https://arxiv.org/abs/2608.04480) | Full method, prompts, main results, aggregation and prompt ablations, cost table, limitations, and release surface checked; no public code found | Charades-STA, ActivityNet Captions, and QVHighlights using three-second frozen-VLM clip scores plus deterministic single/multi-interval readouts | mIoU, R@tIoU, moment mAP, highlight mAP/Hit@1, tokens, throughput, and transient memory | Best-isolated recent evidence for separating recognition from boundary extraction. The test uses many 7B/8B VLM calls, validation-selected aggregation, and a preprint submitted four weeks before this audit. | +| [STITCH](https://arxiv.org/abs/2608.27929) | Full method, application tables, hyperparameters, compute notes, and anonymized artifact link checked | Generic event boundaries, ActivityNet/QVHighlights moment retrieval, and long-video QA using reusable InternVideo2 change-point chunks | Boundary F1, moment R@1/tIoU and mIoU/mAP, and QA accuracy deltas | Closest method to a reusable offline temporal index. It is a days-old NeurIPS submission, uses task-set post-processing choices and an RTX 5080 for feature extraction, and lacks a stable public release. | +| [Lighthouse](https://aclanthology.org/2024.emnlp-demo.6/) | Full paper, official repository, checkpoints/API, CPU path, license, and input limit checked | Reproduces DETR-family moment/highlight models and now includes AM-DETR plus CASTELLA audio support | Reproduction deltas, task metrics, and inference examples | Apache-2.0 and CPU inference are favorable. The 150-second guard applies to Lighthouse's video encoder. CASTELLA trains and evaluates audio sequences of at most 300 one-second features; its loader truncates longer sequences, so this is a five-minute model horizon rather than unrestricted long-audio support. Its pinned dependencies conflict with VidXP's current runtime. | +| [NumPro](https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Number_it_Temporal_Grounding_Videos_like_Flipping_Manga_CVPR_2025_paper.html) | Full paper, training-free/fine-tuned results, marker-design ablations, and official code checked | Standard VTG datasets using frame-number overlays with video LLMs | Moment/highlight metrics under training-free and fine-tuned settings | Demonstrates that direct timestamp generation benefits from explicit visual indices. It modifies frames and serves a video-LLM path, not VidXP's reusable multimodal index. | +| [Moment-GPT](https://arxiv.org/abs/2501.07972) | Full method, main tables, component/hyperparameter ablations, efficiency appendix, and release surface checked | QVHighlights, Charades-STA, and ActivityNet Captions using LLaMA-3 rewriting, MiniGPT-v2 span generation, VideoChatGPT scoring, and NMS | Moment R@tIoU, mIoU/mAP, highlight metrics, OOD results, and oracle bounds | Thorough zero-shot pipeline but computationally broad: several frozen LLM/MLLM stages run per query. Its selected rewrite count, span thresholds, and NMS settings are not a lightweight general boundary rule. | +| [BOLT](https://openaccess.thecvf.com/content/CVPR2025/html/Liu_BOLT_Boost_Large_Vision-Language_Model_Without_Training_for_Long-form_Video_CVPR_2025_paper.html) | Full paper, supplement, and official repository checked | Video-MME, LongVideoBench, MLVU, and multi-source noisy-video evaluation using CLIP query-frame similarity | Downstream VQA accuracy at fixed frame budgets | Inverse-transform sampling improves frame selection without training. It consumes pre-extracted frame features and returns selected frames, not start/end intervals, so it cannot resolve VidXP's boundary error alone. | +| [Automatic Funny Scene Extraction](https://ojs.aaai.org/index.php/AAAI/article/view/41480) | Full paper and official publication record checked | TransNetV2 shots; MovieNet-SSeg and OVSD scene boundaries; humor datasets; curator review on five movies and 11 trailers | Scene AP/F1, humor F1/accuracy, and curator judgments | Relevant applied pipeline: segment into semantic scenes before task-specific ranking. The reported 98% proper scene ending is a five-movie human judgment, not arbitrary-query IoU; no public end-to-end implementation or checkpoint was identified. | +| [FunnyNet](https://openaccess.thecvf.com/content/ACCV2022/papers/Liu_FunnyNet_Audiovisual_Learning_of_Funny_Moments_in_Videos_ACCV_2022_paper.pdf) and [FunnyNet-W](https://link.springer.com/article/10.1007/s11263-024-02000-2) | Both full papers and the public project/code pages checked | TBBT, MHD, MUStARD, Friends, UR-Funny, and in-the-wild humor examples | Dataset-specific funny-moment classification/detection measures | Establish an audiovisual, later audio-visual-text, humor-detection lineage and report audio as especially useful. The learned objective is domain-specific and is not evidence for arbitrary event queries. | +| [AVicuna](https://ojs.aaai.org/index.php/AAAI/article/view/32784) | Full paper and official publication record checked | PU-VALOR with 114,081 pseudo-untrimmed examples plus audiovisual QA and dense localization tasks | Task-specific QA and dense temporal-localization measures | Shows that one trained model can align audio, video, text, and intervals. Its training/data/model scale makes it a research ceiling, not a first commodity-hardware integration. | + ## Whole-system and multimodal benchmark definitions | Paper or specification | Evidence checked | Actual benchmark protocol | Measures/results reported | Validation outcome | | --- | --- | --- | --- | --- | -| [LongVALE](https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.pdf) | Full text, repository, evaluator path, and current release tree checked | 8,411 videos, 105,730 vision/audio/language events, and more than 549 hours; the evaluation split has 1,171 videos, 13,867 events, and 75.6 hours for known-video temporal grounding, dense event captioning, and segment captioning | Grounding R@1 at tIoU 0.3/0.5/0.7 and mean IoU; SODA-c, CIDEr, METEOR; BLEU-4 and ROUGE-L where applicable | Strongest peer-reviewed combined temporal target found. Omni-TVG evaluates one interval per event query, represented in the reference path as integer 0ā€“99 percentagesā€”not a generic top-k list. Raw evaluation ZIPs are 40.523 GiB and ZIPs plus extracted MP4s are 81.186 GiB before VidXP artifacts. The roughly 254 GB headline is the full repository. Released features are only inputs to the official LongVALE-LLM path, which also needs its model weights/environment; they bypass VidXP. | +| [LongVALE](https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.pdf) | Full text, repository, evaluator path, and current release tree checked | 8,411 videos, 105,730 vision/audio/language events, and more than 549 hours; the evaluation split has 1,171 videos, 13,867 events, and 75.6 hours for known-video temporal grounding, dense event captioning, and segment captioning | Grounding R@1 at tIoU 0.3/0.5/0.7 and mean IoU; SODA-c, CIDEr, METEOR; BLEU-4 and ROUGE-L where applicable | Strongest peer-reviewed combined temporal target found. Omni-TVG evaluates one interval per event query, represented in the reference path as integer 0ā€“99 percentagesā€”not a generic top-k list. Section 3.2 constructs semantic visual/audio events before multimodal boundaries, but the official repository still lists that annotation-pipeline code as unreleased. Raw evaluation ZIPs are 40.523 GiB and ZIPs plus extracted MP4s are 81.186 GiB before VidXP artifacts. The roughly 254 GB headline is the full repository. Released features are only inputs to the official LongVALE-LLM path, which also needs its model weights/environment; they bypass VidXP. | | [FLARE](https://arxiv.org/abs/2605.10228) | Full text, repository, pinned release, and artifact tree checked | 399 Video-MME-source videos, 225.4 hours, 87,697 clips, and 274,933 model-simulated queries. Caption evaluation supports text-to-clip/video and reverse retrieval; generated-query evaluation is clip-level only | R@1/5/10 | Catalog corrected: query-regime text-to-video was not evaluated. The benchmark ZIP+JSONL artifacts are 66.266 GiB and the complete pinned revision is 66.267 GiB. Rank-filtered simulated queries do not establish human-query generalization, and speech-only coverage is incomplete because audio queries also include music and sound events. | | [MultiVENT 2.0](https://openaccess.thecvf.com/content/CVPR2025/papers/Kriz_MultiVENT_2.0_A_Massive_Multilingual_Benchmark_for_Event-Centric_Video_Retrieval_CVPR_2025_paper.pdf) | Full text and official download/evaluator pages checked | Ranked retrieval over 218,300 videos with more than 3,900 professionally written queries using visual, speech/ASR, embedded-text/OCR, and human-description metadata evidence; TEST-NO-DESC and TEST-DESC | R@10, R@100, MRR, mAP, and nDCG@10 | Catalog corrected: this is not generic acoustic-audio retrieval. Only 39% Judged@10 and pooled judgments make unjudged-as-zero/model-pool bias material. | | [TRECVID Ad-hoc Video Search overview](https://trec.nist.gov/pubs/trec33/papers/Overview_avs_vtt_actev.pdf) | Primary 2024 overview, 2025 task specification, and V3C source checked | Sentence query to up to 1,000 ranked V3C2 master shots; 9,760 videos, 1,300 hours, and 1,425,454 shots | Mean xinfAP plus elapsed seconds per query | Archived reusable 2024/2025 protocol, not an active 2026 task. Historical topics/tools exist, but qrels must be tied to the exact year. | @@ -101,11 +149,13 @@ paper-level ledger row below; the final coverage check found zero omissions. | --- | --- | --- | --- | --- | | [Localizing Moments in Video with Natural Language](https://arxiv.org/abs/1708.01641) | Full paper and official evaluator checked | Introduces DiDeMo: a known 25ā€“30 second video and query rank 21 contiguous moments built from six five-second chunks | Rank@1, Rank@5, and mean IoU with multiple human annotations | Benchmark-defining fixed-grid localization only; not unrestricted boundaries or corpus retrieval. | | [Moment-DETR](https://proceedings.neurips.cc/paper/2021/hash/62e0973455fd26eb03e91d5741a4a3bb-Abstract.html) | Full paper and official artifacts checked | Introduces QVHighlights joint moment retrieval and highlight detection; 10,310 queries, 18,367 moments, 10,148 videos in the defining paper | Moment mAP@0.5/0.75 and average mAP 0.50:0.05:0.95; R@1 at tIoU 0.5/0.7; highlight mAP and Hit@1 | Saliency is defined on two-second clips, not arbitrary frames. Every result must name the test-label release because the original paper used private/CodaLab labels while later artifacts expose test ground truth. | -| [Zero-shot Video Moment Retrieval With Off-the-Shelf Models](https://proceedings.mlr.press/v203/diwan23a.html) | Full paper checked | QVHighlights only, using a 1,434-video downloadable filtered validation subset; shot proposals, one-fps CLIP, optional captions, and watershed merging | QVHighlights moment and highlight metrics on the filtered split | Closest zero-shot comparator, but its headline system is more than raw CLIP. Numbers require the identical `val-filt` subset or a rerun. | +| [Zero-shot Video Moment Retrieval With Off-the-Shelf Models](https://proceedings.mlr.press/v203/diwan23a.html) | Full paper checked | QVHighlights only, using a 1,434-video downloadable filtered validation subset; shot proposals, one-fps CLIP, optional captions, and watershed merging | QVHighlights moment and highlight metrics on the filtered split | The full visual recipe uses shot sensitivity `lambda = 32` and merges consecutive proposals whose CLIP cosine is at least `gamma = 0.7`; its no-postprocessing control uses `lambda = 53`. VidXP's existing control only reproduced the latter proposal setting, substituted SigLIP 2, and omitted watershed. The published threshold is therefore not portable to VidXP's score scale without held-out calibration. | | [TALL / CTRL](https://arxiv.org/abs/1705.02101) | Full paper checked | TACoS and introduced Charades-STA, including a 1,378-query complex test set | R@1/R@5 at IoU 0.5/0.7 for Charades-STA; 0.1/0.3/0.5 for TACoS | Benchmark-defining for Charades-STA. Original 13,898/4,233 and later filtered 12,408/3,720 splits are not interchangeable. | | [Towards a Complete Benchmark on Video Moment Localization](https://proceedings.mlr.press/v238/chae24a.html) | Full paper checked | ActivityNet Captions, Charades-STA, DiDeMo, TACoS, YouCook2, MSR-VTT, and TVR in the MoLEF framework | Unified per-dataset grounding and cost/bias analyses | Evaluation-methodology paper, not a new dataset or VidXP-like zero-shot baseline. Exact adapted split files must be taken from its repository before reproduction. | | [QD-DETR](https://github.com/wjun0830/QD-DETR) | Full paper/repository checked | QVHighlights and Charades-STA for moment retrieval; QVHighlights and TVSum for highlight detection | Official task-specific moment/highlight metrics | Supervised comparator. It does not experimentally cover Ego4D, TACoS, DiDeMo, MSR-VTT, or ActivityNet Captions. | | [UniVTG](https://github.com/showlab/UniVTG) | Full paper/repository checked | QVHighlights; Ego4D NLQ, Charades-STA, TACoS; YouTube Highlights, TVSum; QFVS. Ego4D/VideoCC/CLIP-generated labels are pretraining sources | Task-specific moment, highlight, and summarization metrics | Broad pretrained/supervised comparator across different temporal-label tasks. Only explicitly marked rows are zero-shot. | +| [HieraMamba](https://openaccess.thecvf.com/content/CVPR2026/html/An_HieraMamba_Video_Temporal_Grounding_via_Hierarchical_Anchor-Mamba_Pooling_CVPR_2026_paper.html) | Full paper and official release checked | Ego4D-NLQ, MAD/MAD-v2, and TACoS long-video grounding | Dataset-specific R@1/R@5 at temporal-IoU thresholds plus efficiency | Directly supports learned multi-scale temporal grounding instead of fixed independent windows. The release uses Mamba and compiled NMS dependencies and does not establish a CPU/Mac path. | +| [UniversalVTG](https://arxiv.org/abs/2604.08522) | Full paper, supplement, checkpoint surface, and official release checked | One shared checkpoint over GoalStep, Ego4D-NLQ, TACoS, Charades-STA, and ActivityNet Captions | R@1/R@5 at dataset-specific temporal-IoU thresholds; 60M-head runtime and feature-extraction cost | Closest technical fit for general long-video action search. It uses 2-fps Perception Encoder features, HieraMamba interval prediction, and an inference-time LLM query unifier. The current release requires Linux/CUDA, Mamba extensions, and has no top-level license file, so it is not yet adoptable. | | [VERIFIED](https://proceedings.neurips.cc/paper_files/paper/2024/hash/477929b8d45ab759795b7aac94329b08-Abstract-Datasets_and_Benchmarks_Track.html) | Full paper, supplement, project, repository, and release state checked | Introduces Charades-FIG, DiDeMo-FIG, ActivityNet-FIG and evaluates HERO, XML, ReLoCLNet, CONQUER, and SQuiDNet on VCMR, VR, and SVMR | Corpus video/moment recall at ranked cutoffs and overlap thresholds | Major fine-grained corpus-moment benchmark, not merely known-video localization. Annotations and pre-extracted features are released, but no implementation/evaluator or explicit repository license was found; the repository's open baseline-implementation request remains unresolved. | | [LoVR](https://arxiv.org/abs/2505.13928) | Full paper, accepted-paper listing, project, repository, and dataset release checked | Introduces long-video text-to-video, video-to-text, text-to-clip, and clip-to-text retrieval over 467 videos and 40,804 predefined clips | Bidirectional R@1/R@5/R@10 | Accepted to The Web Conference 2026, correcting the earlier arXiv-watchlist status. The paper says all data are test-only, while the current Hugging Face release exposes differently named/counting splits; execution must pin a revision and reconcile this conflict. | | [MAD](https://arxiv.org/abs/2112.00431) | Full paper and official repository checked | Introduces 384,000 audio-description queries over 650 movies and more than 1,200 hours for long-movie grounding | Recall at temporal-IoU thresholds | Strong movie-domain target, but annotations/features do not include raw movies. Exact evaluator K/tIoU table must be copied from the official code before implementation. | @@ -149,7 +199,7 @@ paper-level ledger row below; the final coverage check found zero omissions. | Source | Evidence checked | Actual protocol | Validation outcome | | --- | --- | --- | --- | -| [IARPA Janus Benchmark-B](https://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/html/Whitelam_IARPA_Janus_Benchmark-B_CVPR_2017_paper.html) | Full text checked | Seven clustering sub-protocols of increasing subject/media scale; each input is an image plus a face box; B-cubed precision, recall, and F-measure | Formal face-clustering protocol, but not a video continuity test. Distribution has ended, so it is executable only if the team already holds a lawful copy. | +| [IARPA Janus Benchmark-B](https://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/html/Whitelam_IARPA_Janus_Benchmark-B_CVPR_2017_paper.html) | Full text and [NIST protocol](https://www.nist.gov/system/files/documents/2021/06/07/ijbb_challenge_documentation_readme.pdf) checked | Seven clustering sub-protocols of increasing subject/media scale; each input is an image plus a face box; B-cubed precision, recall, and F-measure | Formal face-clustering protocol, but not a video continuity test. Distribution has ended, so it is executable only if the team already holds a lawful copy. | ## Cross-cutting corrections diff --git a/docs/benchmarking/published_results.md b/docs/benchmarking/published_results.md index de30385e..0f70b979 100644 --- a/docs/benchmarking/published_results.md +++ b/docs/benchmarking/published_results.md @@ -4,7 +4,7 @@ Collection index: [Benchmarking research](README.md) Status: Primary-source result extraction complete -Last verified: 2026-08-27 +Last verified: 2026-09-04 This is the answer to ā€œwhat did the published competitors actually score?ā€ It is the result-level companion to the capability matrix in the @@ -50,8 +50,36 @@ proceedings pp. 10398ā€“10399. | LAION-CLAP | 35.1 | 44.2 | Mature native-Transformers integration baseline | The same paper uses fixed ten-second FineLAP inputs and identifies variable-length -audio as future work. Long-media integration therefore still needs timestamped -windowing, overlap, and span merging owned by VidXP. +and long-form modeling as future work. Its global and local representations can +support clip retrieval and event-phrase scoring; the paper does not validate a +long-audio selector built by ranking those windows independently. + +### DCASE 2026: natural-language retrieval in long audio + +Source: [official Task 6 final results](https://dcase.community/challenge2026/task-audio-moment-retrieval-from-long-audio-results), +checked 2026-09-04. The hidden evaluation has 177 queries over 100 recordings; +the primary metric is top-one recall at temporal IoU 0.7. + +| System | Main representation and interval model | Total parameters | Hidden R1@0.5 | Hidden R1@0.7 | Artifact status | +| --- | --- | ---: | ---: | ---: | --- | +| Official baseline | MS-CLAP + QD-DETR | 165.5M | 28.25 | 13.56 | MIT code; released training data and features | +| Kibata et al. | M2D-CLAP + modified CG-DETR | 211.87M | 69.49 | 48.59 | Technical report only; no public code or checkpoint verified | +| Kim et al. | Multiple encoders + QAM-DETR + 7B audio LLM | 11.19B | 63.84 | 48.59 | Too large for the 8 GB CPU target | +| Sugawara et al. | MS-CLAP/M2D-CLAP + UVCOM ensemble | 603.8M | 59.89 | 48.59 | Ensemble entry; exact submitted weights not verified | + +This is the direct comparison for VidXP's current sound failure. All leading +systems consume a temporal audio-feature sequence and predict interval endpoints +and confidence jointly. The smallest tied winner adds 13.37M trainable parameters +to a 198.5M frozen M2D-CLAP encoder. Its score establishes the architecture and +encoder direction, but missing released weights prevent a product adoption claim. + +[CASTELLA](https://arxiv.org/abs/2511.15131) provides a released reproduction +control: its official Lighthouse UVCOM checkpoint reports R1@0.7 20.3 on the +CASTELLA test split. That is a trained long-audio result, not directly comparable +with FineLAP's clip-retrieval R@1. CASTELLA also reports a marked weakness on +moments shorter than ten seconds, which includes VidXP's four sound pilot events. +Its published quality and conflicting dependencies do not justify a separate +product runtime; use it only when that reproduction is explicitly needed. ### MVEB: current text-video embedding comparison @@ -74,11 +102,13 @@ checked 2026-08-27. | Checkpoint | Seven-dataset average mIoU | Selection use | | --- | ---: | --- | -| TimeLens2-4B | 47.7 | First visual temporal-grounding candidate after cheap recall | +| TimeLens2-4B | 47.7 | Recent visual temporal-grounding ceiling; no default selection without task-fit and runtime comparison | | TimeLens2-8B | 48.0 | Quality ceiling; not the practical default for a 0.3-point gain | Both checkpoints are visual-only. Neither evaluates environmental audio or spoken -content, so neither can cover LongVALE's full task alone. +content, so neither can cover LongVALE's full task alone. These results make +TimeLens2 a recent ceiling; they do not select it over older, smaller, released +temporal-grounding systems for VidXP. ### AEGBench: open-vocabulary sound boundaries @@ -95,6 +125,23 @@ including repeated occurrence, polyphonic overlap, gradual boundaries, and long duration. It is a component benchmark, not evidence of end-to-end visual/sound/ speech fusion. +### Do not compare frame selection, scene endings, and interval IoU + +Three relevant research lines report different outputs and metrics: + +| Work | Reported result | Correct interpretation | +| --- | --- | --- | +| [BOLT](https://openaccess.thecvf.com/content/CVPR2025/html/Liu_BOLT_Boost_Large_Vision-Language_Model_Without_Training_for_Long-form_Video_CVPR_2025_paper.html) | The paper reports Video-MME accuracy increasing from 53.8 to 56.1 and MLVU from 58.9 to 63.4 under query-aware frame selection | VQA accuracy under a frame budget. BOLT returns selected frames, not an event interval, so these numbers cannot be compared with temporal IoU. | +| [Automatic Funny Scene Extraction](https://ojs.aaai.org/index.php/AAAI/article/view/41480) | 18.3% relative AP improvement on OVSD, humor F1 0.834, 87% intended-funny curator judgment, and 98% proper scene localization on five full titles | Evidence for an applied shot-to-scene-to-ranking pipeline. The 98% is curator judgment of whether extracted scenes ended properly, not overlap with arbitrary natural-language intervals. | +| [Off-the-Shelf VMR](https://proceedings.mlr.press/v203/diwan23a.html) | On its released 1,434-video QVHighlights validation subset, ShotDetect + CLIP + SimpleWatershed reaches R1@.5 48.33 and R1@.7 30.96 | Direct evidence that proposal construction changes zero-shot moment retrieval. It is subset-specific and has no official end-to-end release. | + +The established scene-segmentation papers and the query-conditioned grounding +papers answer different questions. LGSS, ShotCoL, BaSSL, and NeighborNet assess +whether adjacent shots form coherent scenes. Moment-DETR, UMT, QD-DETR, and +UniVTG assess whether a query identifies one or more moments. UMT and QD-DETR +also evaluate aligned audio features; visual-only grounding is not the only +published design. + ## Whole-system and multimodal retrieval ### LongVALE: known-video omni-modal temporal grounding diff --git a/docs/benchmarking/research_adoption.md b/docs/benchmarking/research_adoption.md new file mode 100644 index 00000000..6ceadb1d --- /dev/null +++ b/docs/benchmarking/research_adoption.md @@ -0,0 +1,180 @@ +# Research adoption record + +Collection index: [Benchmarking research](README.md) + +Status: Current source of truth + +Last verified: 2026-09-06 + +This page records which published ideas are in VidXP, where they are used, and +where VidXP deviates. The [paper inventory](research_papers.md) and +[validation ledger](paper_validation.md) cover reviewed work that is not +adopted. + +## Status meanings + +- **Adopted**: used in the current product path. +- **Control**: current behavior retained for comparison, without a claim that + it is the final design. +- **Experiment**: benchmark-only code, not product behavior. +- **Candidate**: reviewed but neither adopted nor implemented. +- **Rejected**: executed and failed a stated product gate. +- **Blocked**: not executed because its artifacts, license, or deployment path + failed first. + +A similar-looking implementation is not paper-derived after the fact. Every +paper-derived change must name the exact source and method, document deviations, +and record the evidence used to accept it. Original VidXP engineering must be +labeled as such. + +## Product adoptions + +| Source | Adopted part and location | Reason | VidXP deviation or limit | +| --- | --- | --- | --- | +| Vyas et al., [PE-AV and PE-A-Frame](https://arxiv.org/abs/2512.19687), CVPR 2026 | PE-A-Frame Small's released audio-frame and text embeddings in `src/vidxp/capabilities/sound/` | Supplies free-form sound-event ranking at the model's 40 ms frame rate | The paper establishes the model and dot-product scoring, not long-media chunking or user-facing intervals. VidXP adds bounded overlapping inference and fixed evidence windows as documented deployment controls. | +| Cormack, Clarke, and Buettcher, [Reciprocal Rank Fusion](https://doi.org/10.1145/1571941.1572114), SIGIR 2009 | Rank-only formula with `k = 60` in `src/vidxp/search_fusion.py` | Combines modality rankings without treating their raw distances as one scale | Rank-anchored candidate construction, direct temporal matching, one hit per supporting modality, and interval union are VidXP controls, not parts of the paper. | +| Zhao et al., [VideoPrism](https://arxiv.org/abs/2402.13217), ICML 2024, and Google's public LvT checkpoint | Global video-text embeddings and official text canonicalization in `src/vidxp/capabilities/action/` | Supplies cross-modal similarity for short action clips | VidXP's fixed windows and long-video ranking are not VideoPrism methods. The paper's action results use task-specific evaluation heads and do not validate raw similarity as temporal action localization. | +| Tschannen et al., [SigLIP 2](https://arxiv.org/abs/2502.14786), 2025 | Released image-text encoder in `src/vidxp/capabilities/scene/` | Supplies visual-semantic frame retrieval | VidXP samples at 1 fps. These records are sampled frames, not detected semantic scenes. | +| Radford et al., [Whisper](https://arxiv.org/abs/2212.04356), ICML 2023, and Zhang et al., [Qwen3 Embedding](https://arxiv.org/abs/2506.05176), 2025 | Speech recognition and text embeddings in `src/vidxp/capabilities/speech/` | Produces timestamped, searchable transcript evidence | `faster-whisper` is the runtime implementation. Segmentation, storage, and retrieval are VidXP choices. | + +Index schema 8 changes the vector metric and sound representation. Rebuild +repositories created by older versions before using this product path. + +## Sound replacement decision + +The sound provider's task is a free-form acoustic description to timestamped +occurrences. Audio moment retrieval measures sentence-to-interval retrieval on +long recordings; open-vocabulary sound-event grounding measures the short, +repeated, and overlapping sounds that also matter to VidXP. Neither task alone +covers the whole multimodal product query. + +| Candidate | Grounded result | Product decision | +| --- | --- | --- | +| PE-AV and PE-A-Frame, Vyas et al., [ā€œPushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learningā€](https://arxiv.org/abs/2512.19687) | Apache-2.0 family. PE-AV jointly embeds audio, video, audio-video, and text; PE-A-Frame produces dense sound-localization scores. | PE-A-Frame Small is selected for sound localization from the frozen AEGBench comparison. PE-AV is not selected for action: the small recognition gate was already at its ceiling, PE-AV has no interval head, and its checkpoint was larger and slower in the smoke. | +| [FlexSED](https://arxiv.org/abs/2509.18606), Hai et al. | MIT, 430.9 MB detector checkpoint plus pinned LAION CLAP; produces 25-fps scores for requested event phrases | Not selected. Runtime passed, but it missed the unique siren and drumbeat targets. The reported `0/4` target score is not a provider-quality rate because phone is invalid and engine has multiple correct occurrences. | +| [DASM](https://arxiv.org/abs/2507.16343), Cai et al. | The official model hub exposes 636 MB of MIT-marked weights, but released text-query inference hard-codes CUDA and depends on a separate MGA-CLAP checkout and checkpoint | Blocked, not benchmarked. The Transformer4SED source repository has no software license, so VidXP must not copy or port its implementation without clarification. | +| [WSTAG](https://arxiv.org/abs/2401.02584), Xu et al. | MIT source and an Apache-2.0 model-hub release provide a CPU code path and 40 ms probabilities; the authors recommend the newer 131.96M-parameter AudioCaps-v2/LAION-CLAP model | Not selected. It missed the unique siren and drumbeat targets at the released threshold. Its engine top result at 242.22 s matches another LongVALE engine-rev annotation, so the current target-only score is not a valid final quality estimate. | +| [FLAM/OpenFLAM](https://arxiv.org/abs/2505.05335), Wu et al. | ICML 2025 frame-wise open-vocabulary detector and retrieval model; the public release supports CPU in its example | Blocked. Code and model are non-commercial, and the public OpenFLAM checkpoint is not the internal model behind the paper's reported results. | +| [SpotSound](https://arxiv.org/abs/2604.13023), Sun et al. | ACM MM 2026 short-event temporal grounder; directly targets false timestamps and needle-in-a-haystack audio | Research ceiling only. Its 80.8 MB adapter requires the 8B non-commercial Audio Flamingo 3 base and a Linux/CUDA-oriented runtime. | +| [TimeAudio](https://arxiv.org/abs/2511.11039), Wang et al. | Long-audio temporal model with explicit time encoding and token merging | Rejected for this deployment before execution: the release requires Vicuna-7B and documents more than 40 GB GPU memory. | +| [Official DCASE 2026 MS-CLAP/QD-DETR baseline](https://dcase.community/challenge2026/task-audio-moment-retrieval-from-long-audio-results) | Direct interval prediction from one-second features; 13.56 R1@0.7 on the hidden evaluation | Reproducibility control, not the product candidate. Its dependencies conflict with the managed runtime. | +| [M2D-CLAP + modified CG-DETR, Kibata et al.](https://dcase.community/challenge2026/task-audio-moment-retrieval-from-long-audio-results) | 211.87M parameters and 48.59 R1@0.7, tied first in DCASE 2026 | Quality target only; public code and weights were not verified. | +| [CASTELLA](https://arxiv.org/abs/2511.15131)-trained UVCOM in [Lighthouse](https://aclanthology.org/2024.emnlp-demo.6/) | Released checkpoint; 20.3 R1@0.7; at most 300 one-second audio features | Reproducibility control only. A second runtime adds install, storage, and support cost without demonstrated product gain. | + +The reference-audio audit found the phone-ring interval at `-91.75 dBFS` RMS +and `-78.27 dBFS` peak despite an explicit ringing annotation. Its MP4 matches +the downloaded archive, so quarantine it from sound-only scoring pending human +review rather than changing its label silently. The engine sound phrase also +has several correct occurrences, including WSTAG's top result inside a separate +LongVALE engine-rev annotation. The old component score therefore has only two +unambiguous cases; FineLAP, FlexSED, and WSTAG miss both. It is superseded for +provider selection by the 149-query AEGBench result. That result justifies +implementing PE-A-Frame Small and rebuilding the sound index; it does not +validate the long-audio serving path. + +VidXP splits overlap ownership at the midpoint, maps retained frames to global +timestamps, and stores each timestamp once. Search ranks frames with the model's +dot product, then returns the best frame from each fixed evidence window. + +| Sound default | Basis | +| --- | --- | +| 10-second inference section | The product-path smoke took 22.156 seconds with ten-second sections versus 198.651 seconds with 60-second sections on the same 75.81-second video. This is a Mac resource choice, not a PE-A accuracy setting. | +| 2-second inference overlap | Smallest nonzero overlap tested; it preserved context across section edges without increasing the measured run time. Five seconds increased the run to 33.564 seconds and did not change the two checked top results. | +| 10-second evidence window | Returns a playable context chunk while retaining the best 40 ms timestamp as metadata. It does not claim an exact event boundary. | +| Batch size 1 | Conservative default after PE-A reached 5.30 GiB peak RSS in the AEGBench provider run. | + +These are configurable VidXP engineering defaults, not PE-A-Frame methods or +measured accuracy optima; the long-audio gate remains required. Distinct +repeated events remain separate windows. + +## Original product controls + +| Behavior | Exact status | +| --- | --- | +| Fixed VideoPrism records | Sixteen frames sampled at 2 fps form a record of about eight seconds. No paper was adopted to select this temporal unit. | +| Raw VideoPrism similarity ranking | Global LvT cosine similarity ranks the fixed records. This is a product control, not the action-localization method evaluated in the paper. | +| One-second SigLIP 2 records | They provide dense visual evidence, not shot or scene boundaries. | +| PE-A frame-to-evidence retrieval | Chroma uses inner product because PE-A ranks frames by dot product. For each requested evidence result, search reads at most the mathematically bounded number of frames that one ten-second window can contain, then keeps the best frame per distinct window. No empirical over-fetch multiplier or score threshold is used. | +| Rank-anchored direct overlap | A hit seeds a candidate and takes at most the best directly overlapping hit from each other modality. Same-modality hits and indirect overlap remain separate. This is VidXP logic. | +| Candidate interval union | A candidate starts at its earliest supporting hit and ends at its latest. A broad source hit can still produce a broad result, but neighboring hits cannot extend it transitively. | +| Separate candidate and output depth | `top_k` limits final fused results. `candidate_top_k` limits each modality to 100 hits by default. The corrected ten-task replay was identical from 100 through exhaustive input; this supports a resource cap, not a general accuracy optimum. | +| Optional query model | A language model may plan searches or summarize citable evidence. Model size and reasoning are deployment choices, not research adoptions. | + +The reverted `4x` over-fetch and anchor-preserving union rule is not adopted. Its +multiplier was selected after one development example and has no general claim. + +## Benchmark-only experiments + +| ID | Source and scope | Recorded result | Decision | +| --- | --- | --- | --- | +| `p2s_asg_vidxp_v1` | [Point-to-Span](https://arxiv.org/abs/2512.10363) v1, Section 3.1 only; VidXP score curves and early NMS replace the unreproduced full pipeline | Development IoU changed from `0.7493` to `0.7976`; only sound produced a span, below the direct-inspection agent's `0.8824` | Concluded diagnostic; not adopted | +| `videoprism_overlap_control_v1` | [CTAP](https://openaccess.thecvf.com/content_ECCV_2018/html/Jiyang_Gao_CTAP_Complementary_Temporal_ECCV_2018_paper.html) and [Barrios et al.](https://openaccess.thecvf.com/content/ICCV2023/html/Barrios_Localizing_Moments_in_Long_Video_Via_Multimodal_Guidance_ICCV_2023_paper.html) motivate overlapping windows; VidXP replaced the normal action index with four-second windows at a two-second stride | On five held-out action tasks, full-list candidate recall at tIoU 0.5 rose from `0.20` to `0.60` and top-1 recall from `0.00` to `0.20`; a top-three coarse gate reduced candidate recall to `0.40` | Overlapping records remain useful candidates. The previous union and the tested coarse gate are rejected; no product selector is adopted | +| `diwan_shotdetect_siglip2_v1` | [Diwan et al.](https://proceedings.mlr.press/v203/diwan23a.html) ShotDetect proposals, scored with existing SigLIP 2 records; VidXP added proposal-level RRF | Development IoU reached `0.8902`; on six scene-comparable held-out tasks RRF reduced mean IoU from `0.2841` to `0.1175` | Proposal-level RRF rejected; code retained as a control | +| `manual_modality_query_ceiling_v1` | [Luo et al.](https://openaccess.thecvf.com/content/WACV2024/html/Luo_Zero-Shot_Video_Moment_Retrieval_From_Frozen_Vision-Language_Models_WACV_2024_paper.html) and [TFVTG](https://arxiv.org/abs/2408.16219) motivate decomposition, but manual modality wording is a VidXP ceiling rather than either published method | Top-three target coverage changed from 7/16 to 8/16; nine ranks improved and two worsened | Mandatory rewriting rejected | +| `finelap_separate_streams_v1` | [FineLAP](https://aclanthology.org/2026.acl-long.473/), Sections 3.2ā€“3.3; global windows and dense activations queried separately | Top-three target coverage changed from 0/4 mixed to 3/4 across separate lists | Supports the product rule not to cross-rank the raw outputs; no local-activation product surface selected | +| `finelap-two-stage-held-out` | [FineLAP](https://aclanthology.org/2026.acl-long.473/)'s two representations with VidXP's global top-three gate and pooled local ranking | Gate coverage `2/4`; final top-three coverage `0/4`; mean final IoU `0` against one accepted interval per task | Exact component diagnostic retained, but invalid labels prevent a provider decision; it does not gate the paired multimodal run | +| `candidate-depth-fusion-control-v1` | Original VidXP diagnostic using saved full-query rankings and production connected-component [RRF](https://doi.org/10.1145/1571941.1572114); RRF supplies only the rank formula | Depth 20 improved R@3 and R@10 at tIoU 0.5 from `0.30` to `0.40` versus depth 3, but R@1 stayed `0.20`. At depth 100 R@1 became `0`; full depth produced video-length top intervals. | No candidate depth adopted. Separate event proposals from ranking; do not replace one shared magic depth with another. | +| `candidate-depth-direct-overlap-control-v2` | The same ten saved full-query rankings after replacing transitive components with rank-anchored direct overlap; only the score formula comes from [RRF](https://doi.org/10.1145/1571941.1572114) | Depths 100 through all were stable instead of collapsing. At full depth, R@1/R@3/R@5/R@10 at tIoU 0.5 were `.10/.10/.20/.20`. | Direct overlap adopted to preserve separate moments. Candidate collection now has an independent default cap of 100; this is not claimed as a general optimum. | +| `pe-a-frame-small-mac-diagnostic` | Vyas et al., [PE-A-Frame Small](https://arxiv.org/abs/2512.19687), exact released checkpoint; one full-track run plus four target-aware recognition clips | Full track: 244.35 s, 4.30 GiB peak RSS, target miss. Target-aware mean best-span IoU: 0.1654 full query, 0.1151 sound-only. | Inconclusive for selection because two sound labels were invalid. Retained as a runtime and failure diagnostic; the AEGBench result supersedes it. | +| `aegbench-sound-seed42-n50` | Vyas et al., [PE-A-Frame Small](https://arxiv.org/abs/2512.19687), versus Li et al., [FineLAP](https://aclanthology.org/2026.acl-long.473/), on 50 frozen [AEGBench](https://huggingface.co/datasets/zihan-audio/AEGBench) recordings; 149 annotated category queries; every repeated interval; provider default thresholds | PE-A versus FineLAP: frame AUROC `.8614/.8401`; frame AP `.7616/.7484`; top-point accuracy `.7651/.7315`; mean IoU `.5226/.2924`; CPU inference `183.30/17.98` s for 613.43 s of audio. | PE-A-Frame Small selected for sound localization. This is a candidate-selection subset, not a full AEGBench score; long-audio stitching remains unvalidated. | +| `kinetics-mini-videoprism-2026-09-05` | [VideoPrism](https://arxiv.org/abs/2402.13217) through VidXP's 2 fps/16-frame records on the pinned 50-video, five-class [Kinetics-mini](https://huggingface.co/datasets/nateraw/kinetics-mini) validation set | Top-1 `50/50`; 390.49 s inference, or 7.81 s/video. A PE-AV Small 16-frame direct-forward smoke classified one archery clip correctly in 13.36 s; its checkpoint is 3,388,082,648 bytes. | Keep VideoPrism. This small gate establishes basic action recognition only; it does not repair or measure long-video temporal ranking. | +| `flexsed-mac-held-out` | Hai et al., [FlexSED](https://arxiv.org/abs/2509.18606), exact detector and LAION CLAP revisions; released non-overlapping ten-second path | 616.7 s audio in 10.85 s; 1.57 GiB peak RSS. Designated target beat surrounding audio on 0/4 full and 0/4 sound-only queries; best target overlap was about 0.045 IoU. | Runtime passes; not selected because it missed both unique valid cases. Overall quality is unscored until repeated sound occurrences are labeled. | +| `dasm-release-compatibility-2026-09-05` | Cai et al., [DASM](https://arxiv.org/abs/2507.16343); official Transformer4SED revision `c3e883d0fbeaf7031b467d45a3c46a88a76c00b6` and official model-hub tree | The hub contains 636 MB of detector/query artifacts. The only released interactive inference is a CUDA notebook with a hard-coded local path and external MGA-CLAP code/weights; the code repository has no license. | Blocked before model execution. This is an artifact, runtime, and licensing failureā€”not a quality result. | +| `wstag-audiocaps-v2-mac-held-out` | Xu et al., [WSTAG](https://arxiv.org/abs/2401.02584), through the authors' newer recommended model `c1ede4afca77acb67bbd20e48e3fc4657b96666a`; LAION CLAP `365dea6ef167def6676140ed93bbc43f84dabb28` | Three audible full tracks: 0/3 designated-target wins in both wording modes; official threshold produced zero designated-target overlaps. Six CPU forwards took 25.82 s and peaked at 4.15 GiB RSS. | Not selected: both unique valid cases were missed. The engine top at 242.22 s is another annotated rev, so no overall provider score is claimed. This is a post-paper checkpoint, not the model reported in 2024. | + +The experiment code lives in `src/vidxp/benchmarks/` and +`benchmarks/codex-mcp/scripts/`. Frozen settings and task data remain beside the +scripts. These controls may be reproduced, but they are not a queue of product +changes. + +## Confirmed conclusions + +- The saved development run found the correct opening region. Its + `0ā€“8.0075`-second result was wider than the `0ā€“6` reference because the + eight-second action record set the component endpoint. +- The overlapping-action control exposed a finer near-target record. It tested + both a replacement index and, in the held-out follow-up, an + eight-second-to-four-second search path. Fine candidate availability improved, + but the coarse gate missed one viable region and similarity ranking usually + did not select the best fine record. +- The pinned Transformers port matches Google's official Flax checkpoint on an + identical 16-frame input: video and text embedding cosine parity rounded to + `1.0`, and all six checked similarity scores differed by less than `0.000051`. + The port is not the observed ranking failure. VidXP did omit the official + query canonicalization; that provider-contract bug is corrected in the + action search path. On the five held-out action tasks, the correction left + mean top-1 IoU at `0.1297` and did not improve any threshold rate; it is a + conformance fix, not the ranking solution. +- FineLAP's global and local records could not be treated as one raw-distance + ranking, and its unvalidated global gate is no longer product behavior. + PE-A-Frame Small now supplies one frame-level score space. FineLAP remains only + in benchmark code for reproducibility. +- RRF is useful as a transparent ranking control, but the current temporal + grouping and union do not provide exact boundaries. +- The original fusion chained adjacent records into video-length moments as + candidate depth increased. Rank-anchored direct overlap removes that failure; + the full-depth replay is now stable. PE-A-Frame, VideoPrism, and SigLIP 2 define + representations, not VidXP's grouping. LongVALE Section 3.2 constructs + single-modal semantic events before combining modalities; that supports the + proposal-first direction but is not a drop-in algorithm for raw records. +- The action replacement must consume a temporal feature sequence and predict + intervals. Another global clip-similarity model does not address the measured + failure. HieraMamba and UniversalVTG directly study this design, but their + released CUDA/Mamba runtime and unresolved repository licensing prevent a + current CPU product adoption. +- The next approved agent comparison should test whether VidXP supplies enough + evidence for a similarly grounded answer with fewer tokens, less time, or + fewer media-inspection calls. IoU remains one diagnostic within that result. + +## Required record for future adoption + +For every paper-derived product change, record: + +1. the exact paper, version, venue, and artifact revision; +2. the adopted method component and product code location; +3. every deviation from the published method; +4. quality and resource evidence supporting the decision; and +5. rejected alternatives and why they lost. + +For original VidXP engineering, state that it is original and record the same +decision evidence. Do not attach a paper citation retroactively. diff --git a/docs/benchmarking/research_papers.md b/docs/benchmarking/research_papers.md index 09911664..e009b691 100644 --- a/docs/benchmarking/research_papers.md +++ b/docs/benchmarking/research_papers.md @@ -2,11 +2,12 @@ Collection index: [Benchmarking research](README.md) -Status: Paper-level benchmark-use audit complete; reading queue active +Status: Paper-level benchmark-use audit active -Last verified: 2026-08-27 +Last verified: 2026-09-05 -Related decision record: [Published benchmark catalog](benchmark_catalog.md) +Related records: [Published benchmark catalog](benchmark_catalog.md) and +[research adoption record](research_adoption.md) This inventory contains papers that introduce a serious candidate benchmark, establish an evaluation protocol, or provide a close baseline for an implemented @@ -17,24 +18,55 @@ The paper-writing team can review these later. This workstream's immediate use i to trace which datasets, metrics, baselines, and public artifacts each paper actually relies on. +## Coverage method and limit + +The pre-2023 search is anchored by the peer-reviewed +[TPAMI survey](https://doi.org/10.1109/TPAMI.2023.3258628) and +[ACM TOMM survey](https://doi.org/10.1145/3532626), which organize temporal +sentence grounding into proposal-based, proposal-free, reinforcement-learning, +and weakly supervised families. The post-2022 update follows the papers and +artifacts in those families through CVPR, ICCV, ECCV, WACV, NeurIPS, ACL, +EMNLP, SIGIR, AAAI, and arXiv through the verification date. + +For the current boundary failure, inclusion requires at least one of: + +- a method for temporal units, candidate generation, boundary inference, or + multimodal combination; +- a zero-shot or deployable interval baseline; or +- an evaluation that can distinguish candidate recall from boundary quality. + +This is a scoped product-research inventory, not a claim that every temporal +grounding paper ever published is listed. Newly found work must be placed in a +method family and checked at method, experiment, artifact, and product-fit +levels; matching a title or abstract is insufficient. + ## Reading order Start with these papers before reviewing individual model variants: 1. **MAEB** and **MVEB** for the current common audio/video embedding landscape. -2. **FineLAP** and **AEGBench** for environmental-sound retrieval and boundaries. +2. **DCASE 2026 Task 6, AM-DETR, and CASTELLA** for free-form queries over + long audio; **PE-A-Frame, DASM, FlexSED, WSTAG, FLAM, SpotSound, TimeAudio, + FineLAP, and AEGBench** for the complementary event-grounding problem. 3. **LongVALE** and **FLARE** for combined long-video vision, sound, and speech. 4. **TVR / XML** for the closest peer-reviewed corpus-level visual/transcript temporal-retrieval task. 5. **Localizing Moments in Video with Natural Language** for the simplest executable visual moment benchmark. -6. **QVHighlights / Moment-DETR** for modern interval and highlight evaluation. -7. **Zero-shot Video Moment Retrieval With Off-the-Shelf Models** for the closest - methodological comparison to VidXP's untuned CLIP retrieval. -8. **HiREST** and **QuerYD** for speech-backed retrieval options. -9. **BCL** for unknown-number video face clustering and its WCP/NMI protocol. -10. **VPCD** and **C1C** for stronger person/track constraints and dataset context. -11. **Towards a Complete Benchmark on Video Moment Localization** for cross-dataset +6. **LGSS, ShotCoL, BaSSL, and NeighborNet** for the established shot-to-scene + segmentation lineage. +7. **QVHighlights / Moment-DETR, UMT, QD-DETR, and UniVTG** for query-conditioned + interval prediction, including established audiovisual input. +8. **BOLT** for query-aware frame selection, kept separate from interval + prediction. +9. **Automatic Funny Scene Extraction**, FunnyNet, and FunnyNet-W for applied + semantic-scene construction and multimodal event ranking. +10. **Diwan et al., Luo et al., TFVTG, REZE, Point-to-Span, and STITCH** for the + zero-shot proposal, boundary, compound-query, and reusable-index families. +11. **HiREST** and **QuerYD** for speech-backed retrieval options. +12. **BCL** for unknown-number video face clustering and its WCP/NMI protocol. +13. **VPCD** and **C1C** for stronger person/track constraints and dataset context. +14. **Towards a Complete Benchmark on Video Moment Localization** for cross-dataset bias and evaluation methodology. ## Current model-selection and modality benchmarks @@ -43,9 +75,19 @@ Start with these papers before reviewing individual model variants: | --- | --- | --- | --- | | [MAEB: Massive Audio Embedding Benchmark](https://arxiv.org/abs/2602.16008) | arXiv 2026 | 30-task MAEB from a 98-task pool; 50+ models | Current common audio-embedding landscape across speech, music, environmental sound, and audio-text work; shows why speech and sound need separate providers | | [MVEB: Massive Video Embedding Benchmark](https://arxiv.org/abs/2606.14958) | arXiv 2026 | 23-task MVEB from a 184-task pool; 33 models | Current common video-embedding comparison, with Qwen3-VL-Embedding leading its text-video table and paired video/audio variants | -| [FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining](https://aclanthology.org/2026.acl-long.473/) | ACL 2026 | AudioCaps, Clotho, classification, sound-event detection, and text-to-audio grounding | Implemented environmental-sound provider because one model exposes both global retrieval and dense localization features | +| [FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining](https://aclanthology.org/2026.acl-long.473/) | ACL 2026 | AudioCaps, Clotho, classification, sound-event detection, and text-to-audio grounding | Historical provider and retained comparison control; its separate representations did not establish VidXP's former global gate | +| [Language-based Audio Moment Retrieval](https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/) | ICASSP 2025 | Clotho-Moment, real UnAV-100 subset, TUT Sound Events 2017; AM-DETR | Direct long-audio text-to-interval task; shows that temporal modeling improves over independently scored sliding windows | +| [CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries](https://arxiv.org/abs/2511.15131) | ICASSP 2026 | 1,862 human-annotated recordings lasting 1ā€“5 minutes; 3,881 captions and 11,308 intervals | Replaces the small real-audio check in the first AMR paper with a public long-audio benchmark and released Lighthouse checkpoints | +| [DCASE 2026 Task 6: Audio Moment Retrieval from Long Audio](https://dcase.community/challenge2026/task-audio-moment-retrieval-from-long-audio-results) | DCASE Challenge 2026 | Hidden evaluation over 100 long recordings; natural-language query to ranked intervals | Current direct leaderboard. The best lightweight entry uses M2D-CLAP plus a query-conditioned DETR span model, not independent window ranking | +| [Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning](https://arxiv.org/abs/2512.19687) | CVPR 2026 | PE-A-Frame on Internal, ASFX-SED, AudioSet Strong, DESED, and UrbanSED event localization | Adopted PE-A-Frame Small provider: Apache-2.0 free-form sound ranking with 40 ms frame embeddings; VidXP's long-media sectioning is separate engineering | +| [Detect Any Sound](https://arxiv.org/abs/2507.16343) | ACM MM 2025 | AudioSet Strong and cross-dataset DESED; DASM | Open-vocabulary event-phrase detector with frame-level localization; research reference only because its released source is unlicensed and its text inference requires CUDA plus external MGA-CLAP code/weights | +| [FlexSED](https://arxiv.org/abs/2509.18606) | WASPAA 2025 | AudioSet Strong with zero- and few-shot event queries | Released open-vocabulary event detector; requires a list of event phrases rather than accepting VidXP's full query as an interval-retrieval request | +| [Towards Weakly Supervised Text-to-Audio Grounding](https://arxiv.org/abs/2401.02584) | IEEE Transactions on Multimedia 2024 | AudioCaps-derived caption and phrase grounding; WSTAG | Established weakly supervised grounding lineage; its newer author-recommended model missed two unique pilot events and exposed invalid single-reference scoring on a repeated engine sound | +| [FLAM: Frame-Wise Language-Audio Modeling](https://arxiv.org/abs/2505.05335) | ICML 2025 | Open-vocabulary frame localization and clip retrieval | Closest compact technical match, but OpenFLAM is non-commercial and the public checkpoint differs from the unavailable internal model behind the paper results | +| [SpotSound](https://arxiv.org/abs/2604.13023) | ACM MM 2026 | Clotho-Moment, UnAV-100, AudioGrounding, SpotSound-Bench, and SED | Direct short-event grounding ceiling; released adapter requires the non-commercial 8B Audio Flamingo 3 base and Linux/CUDA path | +| [TimeAudio](https://arxiv.org/abs/2511.11039) | arXiv 2025 | Temporal grounding, dense captioning, and long-audio tasks | Direct long-audio reference; released Vicuna-7B stack requires more than 40 GB GPU memory | | [Auto-AEG and AEGBench](https://arxiv.org/abs/2607.04383) | arXiv 2026 | Open-vocabulary audio-event grounding and AEGBench | Direct sound-interval benchmark for hard, repeated, and overlapping environmental events | -| [TimeLens2](https://github.com/MCG-NJU/TimeLens2) | arXiv 2026 | Seven visual temporal-grounding datasets | Supports the 4B visual-localizer choice; it has no audio input and cannot cover LongVALE alone | +| [TimeLens2](https://github.com/MCG-NJU/TimeLens2) | arXiv 2026 | Seven visual temporal-grounding datasets | Recent visual-only ceiling with released checkpoints; not an established default or a complete LongVALE solution | | [Robust and Efficient Video Scene Detection using Optimal Sequential Grouping](https://research.ibm.com/publications/robust-and-efficient-video-scene-detection-using-optimal-sequential-grouping) | ISM 2016 | Introduces OVSD | Open-licensed semantic scene-boundary source; useful for segmentation only, not query retrieval, actions, sound, or speech | ## Multimodal and whole-system retrieval @@ -62,6 +104,7 @@ Start with these papers before reviewing individual model variants: | [SAVE: Speech-Aware Video Representation Learning for Video-Text Retrieval](https://openaccess.thecvf.com/content/CVPR2026/html/Zhao_SAVE_Speech-Aware_Video_Representation_Learning_for_Video-Text_Retrieval_CVPR_2026_paper.html) | CVPR 2026 | MSR-VTT-9k/7k, VATEX, Charades, LSMDC | Speech-aware whole-video retrieval, but not timestamp localization | | [LongVALE](https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_LongVALE_Vision-Audio-Language-Event_Benchmark_Towards_Time-Aware_Omni-Modal_Perception_of_Long_Videos_CVPR_2025_paper.pdf) | CVPR 2025 | Introduces LongVALE | Strongest peer-reviewed visionā€“audioā€“speech temporal target; no actor task | | [MultiVENT 2.0](https://openaccess.thecvf.com/content/CVPR2025/papers/Kriz_MultiVENT_2.0_A_Massive_Multilingual_Benchmark_for_Event-Centric_Video_Retrieval_CVPR_2025_paper.pdf) | CVPR 2025 | Introduces MultiVENT 2.0 | Large-corpus visual, speech/ASR, embedded-text/OCR, and description-metadata retrieval; whole videos rather than moments | +| [Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods](https://doi.org/10.1145/1571941.1572114) | SIGIR 2009 | TREC ranked-list fusion experiments | Original source for VidXP's adopted rank-only fusion and `k = 60`; it does not define temporal grouping or interval boundaries | | [MMMORRF: Multimodal Multilingual Modularized Reciprocal Rank Fusion](https://doi.org/10.1145/3726302.3730157) | SIGIR 2025 | MultiVENT 2.0 and TVR | Direct frames+OCR+ASR fixed-fusion pipeline comparator with exact full-test results | | [MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed](https://arxiv.org/abs/2506.09409) | arXiv/MAGMaR presentation 2025 | MultiVENT 2.0 official shared-task test | Provides zero-shot and target-trained unified-embedding results plus a released checkpoint; no archival workshop paper was found | | [Q2E: Query-to-Event Decomposition for Zero-Shot Multilingual Text-to-Video Retrieval](https://aclanthology.org/2025.ijcnlp-long.121/) | IJCNLP-AACL 2025 | Original MultiVENT, MSR-VTT 1k-A, and MSVD | Training-free visual/Whisper rank-fusion comparator; the original MultiVENT protocol must not be conflated with MultiVENT 2.0 | @@ -80,6 +123,7 @@ portable judged benchmark covering all of VidXP. | --- | --- | --- | --- | | [Multi-modal Video Search by Examples: A Video Quality Impact Analysis](https://pure.ulster.ac.uk/ws/files/222412425/IET_Computer_Vision_-_2024_-_Wu_-_Multi_modal_video_search_by_examples_A_video_quality_impact_analysis.pdf) | IET Computer Vision 2024 | Faces, scenes, speakers, ASR, fusion, approximate search over BBC video | Closest functional analogue; BBC data and judgments are not portable | | [WISE: A Multimodal Search Engine for Visual Scenes, Audio, Objects, Faces, Speech, and Metadata](https://www.robots.ox.ac.uk/~vgg/publications/2026/sridhar2026wise/) | SIGIR 2026 | Scene/object/face, acoustic event, WhisperX speech, metadata, composite queries | Open-source system; deployments and latency context, no portable judged protocol | +| [Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection](https://aclanthology.org/2024.emnlp-demo.6/) | EMNLP Demo 2024 | Six MR-HD models, three feature families, and five datasets behind one API | Best maintained reproduction surface for a trained interval control; CPU inference exists, with a 150-second video limit and CLIP-only CPU guidance | | [ContextIQ](https://openaccess.thecvf.com/content/WACV2025/html/Chaubey_ContextIQ_A_Multimodal_Expert-Based_Video_Retrieval_System_for_Contextual_Advertising_WACV_2025_paper.html) | WACV 2025 | Video, audio, transcript, and metadata experts | Whole-video reference; supplemental annotations but no public implementation | | [Collaborative Experts](https://www.robots.ox.ac.uk/~vgg/research/collaborative-experts/) | BMVC 2019 | Appearance, motion, scene, ASR, OCR, audio experts | Public models/features and corrected results; whole-video task | | [Multi-Modal Transformer for Video Retrieval](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490205.pdf) | ECCV 2020 | RGB, motion, scene, face, OCR, speech, audio experts | Multi-stream whole-video retrieval context | @@ -115,11 +159,33 @@ infrastructure make it unsuitable as the first executable benchmark. | --- | --- | --- | --- | | [Localizing Moments in Video with Natural Language](https://arxiv.org/abs/1708.01641) | ICCV 2017 | Introduces DiDeMo | Defines the simplest first visual test and its 21-moment evaluator | | [Moment-DETR: End-to-End Video Moment Retrieval with Natural Language](https://proceedings.neurips.cc/paper/2021/hash/62e0973455fd26eb03e91d5741a4a3bb-Abstract.html) | NeurIPS 2021 | Introduces QVHighlights | Primary modern interval/saliency benchmark | +| [UMT: Unified Multi-Modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://openaccess.thecvf.com/content/CVPR2022/html/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.html) | CVPR 2022 | QVHighlights, Charades-STA, YouTube Highlights, TVSum | Established query-conditioned interval/highlight model with aligned visual and audio features; official code and checkpoints exist | | [Zero-shot Video Moment Retrieval With Off-the-Shelf Models](https://proceedings.mlr.press/v203/diwan23a.html) | Transfer Learning for NLP Workshop, PMLR 2023 | QVHighlights filtered validation set (1,434 videos) | Nearest zero-shot comparison, but its shot proposals and watershed merging go beyond raw frame-level CLIP scoring | +| [Zero-Shot Video Moment Retrieval From Frozen Vision-Language Models](https://openaccess.thecvf.com/content/WACV2024/html/Luo_Zero-Shot_Video_Moment_Retrieval_From_Frozen_Vision-Language_Models_WACV_2024_paper.html) | WACV 2024 | Charades-STA, ActivityNet Captions, and TACoS, including OOD splits | Strict zero-shot proposal method: query-conditioned frozen features, clustering, and bottom-up combination for compound queries; hyperparameters were selected on Charades-STA and no official code was found | +| [Training-free Video Temporal Grounding using Large-scale Pre-trained Models](https://arxiv.org/abs/2408.16219) | ECCV 2024 | Charades-STA and ActivityNet Captions, including cross-dataset/OOD tests | Official-code compound-query baseline using LLM sub-event ordering plus VLM dynamic/static proposal scoring; adds query-time large-model work | | [TALL: Temporal Activity Localization via Language Query](https://arxiv.org/abs/1705.02101) | ICCV 2017 | Introduces Charades-STA | Established, relatively manageable known-video interval benchmark | | [Towards a Complete Benchmark on Video Moment Localization](https://proceedings.mlr.press/v238/chae24a.html) | AISTATS 2024 | ActivityNet Captions, Charades-STA, DiDeMo, TACoS, YouCook2, MSR-VTT, TVR; MoLEF framework | Cross-dataset bias, cost, and benchmark-methodology review; not a new dataset or zero-shot baseline | | [QD-DETR: Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://github.com/wjun0830/QD-DETR) | CVPR 2023 | QVHighlights, Charades-STA, TVSum | Supervised moment/highlight comparator; no experimental Ego4D, TACoS, DiDeMo, MSR-VTT, or ActivityNet result | | [UniVTG: Towards Unified Video-Language Temporal Grounding](https://github.com/showlab/UniVTG) | ICCV 2023 | QVHighlights, Ego4D NLQ, Charades-STA, TACoS, YouTube Highlights, TVSum, QFVS | Broad pretrained/supervised temporal-label comparator; only explicitly marked rows are zero-shot | +| [HieraMamba: Video Temporal Grounding via Hierarchical Anchor-Mamba Pooling](https://openaccess.thecvf.com/content/CVPR2026/html/An_HieraMamba_Video_Temporal_Grounding_via_Hierarchical_Anchor-Mamba_Pooling_CVPR_2026_paper.html) | CVPR 2026 | Ego4D-NLQ, MAD/MAD-v2, TACoS | Direct response to fixed-window and over-downsampling failures in long video; released Mamba/NMS runtime is CUDA-oriented | +| [UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding](https://arxiv.org/abs/2604.08522) | arXiv 2026 | GoalStep, Ego4D-NLQ, TACoS, Charades-STA, ActivityNet Captions | Closest single-checkpoint long/short-video grounder; 60M grounding head over 2-fps Perception Encoder features, with an inference-time query unifier | +| [Anchor-Aware Similarity Cohesion in Target Frames Enables Predicting Temporal Moment Boundaries in 2D](https://openaccess.thecvf.com/content/CVPR2025/html/Tan_Anchor-Aware_Similarity_Cohesion_in_Target_Frames_Enables_Predicting_Temporal_Moment_CVPR_2025_paper.html) | CVPR 2025 | QVHighlights, Charades-STA, and ActivityNet Captions | Official-code supervised boundary model around the highest-relevance frame; strong boundary ablations, but visual-only and dataset-specific | +| [Number It: Temporal Grounding Videos Like Flipping Manga](https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Number_it_Temporal_Grounding_Videos_like_Flipping_Manga_CVPR_2025_paper.html) | CVPR 2025 | Standard VTG benchmarks with training-free and fine-tuned video-LLM settings | Makes timestamps visually legible by overlaying frame numbers; useful direct-MLLM control but changes media and does not use reusable indexed evidence | +| [Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models](https://arxiv.org/abs/2501.07972) | arXiv 2025 | QVHighlights, ActivityNet Captions, and Charades-STA | Moment-GPT rewrites queries, generates spans, and scores them with several frozen models; high query-time complexity and no accepted venue verified | +| [Point to Span: Zero-Shot Moment Retrieval for Navigating Unseen Hour-Long Videos](https://arxiv.org/abs/2512.10363) | arXiv 2025 | MAD and MomentSeeker | Training-free adaptive peak expansion plus ordered-subquery refinement for hour-long video; highly relevant search-then-refine method, but no official code was found | +| [GranAlign: Granularity-Aware Alignment Framework for Zero-Shot Video Moment Retrieval](https://arxiv.org/abs/2601.00584) | AAAI 2026 | QVHighlights, Charades-STA, and ActivityNet Captions | Training-free dual-granularity query rewrite and query-aware caption alignment; accuracy evidence is useful but adds query-time LLM/VLM work and lacks checked official code | +| [UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding](https://arxiv.org/abs/2604.08522) | arXiv 2026 | GoalStep-StepGrounding, Ego4D-NLQ, TACoS, Charades-STA, and ActivityNet Captions | One cross-dataset-trained interval model with official checkpoint/API; current evaluation and feature extraction require CUDA and a separately licensed upstream component | +| [REZE: Recognition-Based Zero-Shot Extraction for Video Temporal Grounding](https://arxiv.org/abs/2608.04480) | arXiv 2026 | Charades-STA, ActivityNet Captions, and QVHighlights | Separates frozen-VLM clip recognition from deterministic single/multi-interval extraction; unusually complete ablations, but very recent, inference-heavy, and no public code found | +| [Training-Free Temporal Abstraction for General Video Understanding](https://arxiv.org/abs/2608.27929) | arXiv 2026, submitted to NeurIPS | Kinetics-GEBD, TAPOS, ActivityNet Captions, QVHighlights, and long-video QA sets | STITCH makes query-independent semantic chunks reusable across retrieval and reasoning; closest fit to an offline index, but days old with only an anonymized submission artifact | +| [BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding](https://openaccess.thecvf.com/content/CVPR2025/html/Liu_BOLT_Boost_Large_Vision-Language_Model_Without_Training_for_Long-form_Video_CVPR_2025_paper.html) | CVPR 2025 | Video-MME, LongVideoBench, MLVU, and a multi-source retrieval setting | Query-aware frame-selection evidence only; it improves downstream VQA but does not emit temporal intervals | +| [A Local-to-Global Approach to Multi-Modal Movie Scene Segmentation](https://openaccess.thecvf.com/content_CVPR_2020/html/Rao_A_Local-to-Global_Approach_to_Multi-Modal_Movie_Scene_Segmentation_CVPR_2020_paper.html) | CVPR 2020 | Introduces MovieScenes and LGSS | Established multimodal shot-to-scene segmentation; architecture context and temporal-unit benchmark, not text-query grounding | +| [Shot Contrastive Self-Supervised Learning for Scene Boundary Detection](https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.html) | CVPR 2021 | MovieNet scene boundaries and AdCuepoints | Efficient self-supervised shot representations; scene-boundary component evidence only | +| [BaSSL: Boundary-aware Self-Supervised Learning for Video Scene Segmentation](https://github.com/kakaobrain/bassl) | ACCV 2022 | MovieNet scene segmentation | Reproducible scene-boundary model with released code and checkpoint; older CUDA-oriented environment | +| [Neighbor Relations Matter in Video Scene Detection](https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Neighbor_Relations_Matter_in_Video_Scene_Detection_CVPR_2024_paper.html) | CVPR 2024 | Public movie-scene datasets | Recent peer-reviewed shot-context method with official code; segmentation rather than query grounding | +| [Automatic Funny Scene Extraction from Long-form Cinematic Videos](https://ojs.aaai.org/index.php/AAAI/article/view/41480) | IAAI 2026 | OVSD, MovieNet-SSeg, humor datasets, five movies, and 11 trailers | Applied shot detection, multimodal scene construction, and humor ranking; 98% proper-ending judgment is not temporal IoU and no public end-to-end artifact was found | +| [FunnyNet: Audiovisual Learning of Funny Moments in Videos](https://openaccess.thecvf.com/content/ACCV2022/papers/Liu_FunnyNet_Audiovisual_Learning_of_Funny_Moments_in_Videos_ACCV_2022_paper.pdf) | ACCV 2022 | TBBT, MHD, MUStARD, Friends, and UR-Funny | Domain-specific audiovisual funny-moment evidence; supports the value of audio but not arbitrary-query retrieval | +| [FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild](https://link.springer.com/article/10.1007/s11263-024-02000-2) | IJCV 2024 | Five humor datasets plus in-the-wild checks | Extends funny-moment detection to visual, audio, and ASR-derived text; code is public but the objective remains humor-specific | +| [Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding](https://ojs.aaai.org/index.php/AAAI/article/view/32784) | AAAI 2025 | Introduces PU-VALOR and AVicuna | Unified audiovisual interval-alignment ceiling; trained 7B-class system rather than a drop-in local baseline | | [VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding](https://proceedings.neurips.cc/paper_files/paper/2024/hash/477929b8d45ab759795b7aac94329b08-Abstract-Datasets_and_Benchmarks_Track.html) | NeurIPS Datasets & Benchmarks 2024 | Introduces Charades-FIG, DiDeMo-FIG, ActivityNet-FIG for corpus moment retrieval | Major fine-grained VCMR robustness test with published baseline tables and released annotations/features; standalone code/evaluator and repository license remain incomplete | | [LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts](https://arxiv.org/abs/2505.13928) | The Web Conference 2026 | Introduces bidirectional long-video and predefined scene-clip retrieval over 467 videos | Accepted benchmark with published zero-shot baselines and public data/code; released split metadata currently conflicts with the paper and must be pinned before execution | | [MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions](https://arxiv.org/abs/2112.00431) | CVPR 2022 | Introduces MAD | Long-film match, but raw movies are not distributed | diff --git a/docs/benchmarking/results.md b/docs/benchmarking/results.md index bb1c9782..30d2c9cc 100644 --- a/docs/benchmarking/results.md +++ b/docs/benchmarking/results.md @@ -6,8 +6,9 @@ This page answers three questions: 2. What do the measurements mean? 3. What can we honestly conclude from them? -Detailed artifacts, hashes, commands, and evaluator behavior remain in the -[adapter validation ledger](adapter_validation.md). +Agent run artifacts and machine profiles are linked from the +[metric database](metric_database.md). Dataset hashes, commands, and evaluator +behavior remain in the [adapter validation ledger](adapter_validation.md). ## Evidence at a glance @@ -18,10 +19,483 @@ Detailed artifacts, hashes, commands, and evaluator behavior remain in the | Legacy full | HiREST | Released test: 776 known-video searches | Predictions generated, not scored | Public test boundaries are placeholders, so local scoring would be meaningless | | Current smoke | DiDeMo | Official test annotation index `0`; one video | Rank@1 **0**, Rank@5 **1**, mean IoU **0** | Real SigLIP2 execution, serialization, and official-evaluator check only | | Current smoke | HiREST | Two declared validation pairs over two videos | R@0.5 **50**, R@0.7 **50** | Real Qwen3 execution, multi-video storage, filtered search, serialization, and official-evaluator check only | +| Current component gate | Kinetics-mini | 50 ten-second videos over five action classes | VideoPrism top-1 **50/50** | Broad-action recognition works; long-video ranking and boundaries are not measured | +| Current component gate | AEGBench frozen subset | 50 recordings; 149 annotated sound queries | PE-A/FineLAP top-point **76.5%/73.2%**; mean IoU **.523/.292** | Select PE-A-Frame Small for sound localization | +| Current product smoke | PE-A bounded sections | One 75.81-second development video; two known sound queries | 1,896 unique frames; both target ten-second windows ranked first; **22.156 s** indexing after model load | Product decoder/runtime/storage/search integration works; long-audio quality is still unmeasured | +| Agent development smoke | Codex MCP ablation | LongVALE-derived task `ZYT-rain-wind-engine`; neutral prompt and three isolated conditions | Every condition achieved bounded-chunk hit **1** and coverage **1**. Against direct local inspection, VidXP used **40.9%** fewer tokens and finished **22.1%** faster. | Corrected harness smoke only; one development task is not a product gate or held-out result. | +| Local-SLM smoke | Local SLM with VidXP | LongVALE-derived task `ZYT-rain-wind-engine`; separate router and planner prompts | Both policies passed Success@1 and Success@3 with three results and one model request | Runtime gate only; the held-out row carries the quality evidence. | +| Local-SLM held-out pilot | Local SLM with VidXP | Nine LongVALE-derived tasks; router and planner; three repetitions | Router/planner Success@3 **15/27 / 19/27**; **155 / 270** average local tokens; **21.765 / 25.544 s** | Limited planning improves the selected pilot and matches direct inspection's quality range without an external-agent call. | +| Current agent held-out pilot | Codex MCP ablation | Nine LongVALE-derived tasks; three conditions; three repetitions; up to three final candidates | All **27/27** matched pairs were valid and scorable. VidXP/direct-local Success@3 was **15/27** versus **18/27**; VidXP used **20.6%** fewer tokens. | Product gate failed on quality. VidXP's visible MCP evidence reached Hit@3 **19/27**, exposing a ranking/agent-selection gap. | +| First agent held-out pilot | Codex MCP ablation | Nine LongVALE-derived tasks; three conditions; three repetitions; one final candidate | Only **17/27** VidXP/direct-local pairs were valid and scorable. | Historical unscored run; filtered comparisons are diagnostic only. | +| Global-only sound diagnostic | Codex MCP ablation | Same development task after filtering sound search to global clips | VidXP-on IoU **0.6000**; VidXP-off IoU **0.8811** | Same answer content with 16.5% fewer VidXP tokens and 11.3% lower latency, but the ten-second sound clip worsened the endpoint | The current-provider rows are deliberately tiny regression runs. Their percentages are not quality estimates and must not be compared with the full -legacy rows. A current full-corpus score has not been run. +legacy rows. The two component gates make provider decisions only. A current +full-corpus score has not been run. The current whole-product pilot failed its +quality gate; the first pilot remains unscored because it failed its +condition-integrity requirement. + +## Current Codex MCP held-out pilot + +Evaluation +[`eval-7VR-2026-09-06T10:58:07`](runs/eval-7VR-2026-09-06T10-58-07.json) +completed all 81 agent runs in 3 h 13 min 41.136 s on `mac-m2-01`. The current +deterministic scorer accepts all runs, so all 27 VidXP/direct-local pairs enter +the product gate. + +| Condition | Success@3 | Success@1 | Average time | Average tokens | Promptfoo cost | +| --- | ---: | ---: | ---: | ---: | ---: | +| VidXP | 15/27 | 15/27 | 81.423 s | 236,060 | $0.403497 | +| Direct local | 18/27 | 18/27 | 99.669 s | 297,310 | $0.544540 | +| Clean user | 16/27 | 16/27 | 247.446 s | 697,139 | $1.346239 | + +The cost column is Promptfoo's pinned same-model estimate, not the Codex plan's +actual charge. Cached tokens are already part of input and reasoning tokens are +already part of output; neither is added again. The exact rate switch and +known cache-write limitation are documented in the +[agent-ablation method](agent_ablation.md#why-promptfoo-is-the-execution-harness). + +VidXP was 18.3% faster and used 20.6% fewer tokens than direct inspection, but +its Success@3 was lower by 3/27, so the product gate **failed**. The agents +returned 1.15 VidXP candidates on average; Success@3 therefore did not improve +over Success@1. + +The MCP-level diagnostic scores the ready evidence tiles actually shown by +`get_job_evidence`. VidXP surfaced a qualifying target region in 9/27 top tiles +and 19/27 top-three sets, with mean best-of-three event coverage `.650`. This +does not change the failed cross-condition verdict. It shows that the immediate +gap is split: 14 runs both surfaced and returned a hit, five surfaced one that +the agent did not return as a qualifying clip, one returned a hit outside the +visible top-three metric, and seven did neither. Of the 12 final-answer misses, +five expose an agent-selection opportunity and seven still require better +retrieval or ranking. + +This run conclusively establishes only the result under this fixed nine-task +pilot: pre-indexed VidXP reduced average agent tokens by 20.6%, latency by +18.3%, and Promptfoo's comparison-cost estimate by 25.9%. It used fewer tokens +in 20/27 matched pairs, was faster in 19/27, and had a lower comparison cost in +19/27, but returned fewer successful final answers. It does not establish +general 55.6% or 70.4% product accuracy, nor prove that forcing three answers +would preserve the baseline's 66.7% score. Those require broader tasks and a +matched rerun after any prompt, skill, or ranking change. + +The next evidence-handoff measurement is deliberately narrower. The neutral +prompt and scorer stay fixed; only the shipped VidXP skill now preserves up to +three distinct ready candidates from the initial ranked evidence. Running +`./benchmarks/codex-mcp/run vidxp` measures that condition alone and reuses this +run's direct-local and clean-user results as frozen controls. Its result is a +later intervention comparison, not a rescore and not a new counterbalanced +three-condition gate. + +## Local-SLM held-out comparison + +Evaluation +[`eval-BSO-2026-09-06T22:02:53`](runs/eval-BSO-2026-09-06T22-02-53.json) +ran the same nine held-out tasks three times under two separate prompts. Both +policies made one local Ollama request, then a deterministic harness returned +three bounded VidXP evidence windows. All 54 cases were valid and scorable. + +| Policy | Success@3 | Success@1 | MRR | Average time | Average local tokens | External-provider cost | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| Modality router | 15/27 (`55.6%`) | 10/27 | `.432` | 21.765 s | 155 | $0 | +| Search planner | 19/27 (`70.4%`) | 11/27 | `.537` | 25.544 s | 270 | $0 | + +The router chose modalities while preserving the full query and VidXP's default +candidate depth. The planner could also rewrite the query and choose a depth +from 3 through 100; it used 10, 20, 30, 35, or 50. Final result count remained +three in both conditions. + +The planner improved Success@3 by four runs for 3.779 seconds and 115 local +tokens per case. It returned at least one success for every task type the +Codex-with-VidXP agent had solved, though its drumbeat result was 1/3 rather than the +agent's 2/3. On this selected pilot its 70.4% score is comparable to direct +local inspection's 66.7%, while its 25.544-second average is below the Codex + +VidXP agent's 81.423 seconds. This is a cross-run descriptive comparison, not a +new paired gate. + +Original evidence-tile boundaries reached the coverage threshold in 15/27 +planner cases. Four more cases passed after the harness expanded returned +points or short spans into the fixed ten-second serving window. That expansion +is the declared product output contract; it is not exact-boundary improvement. +The planner does not interpret evidence or repair VidXP ranking, and car plus +siren still missed in all three repetitions. The +[metric database](metric_database.md#local-slm-held-out-comparison) gives the +task-level breakdown and the limits on the paper claim. + +The preceding one-task runtime gate is retained as +[`eval-Com-2026-09-06T22:35:55`](runs/eval-Com-2026-09-06T22-35-55.json). +Router and planner both passed; they used 155 and 292 tokens and completed in +36.798 and 43.789 seconds respectively. This smoke is not part of the held-out +score. + +## First Codex MCP held-out pilot + +Evaluation +[`eval-dxR-2026-09-06T00:15:35`](runs/eval-dxR-2026-09-06T00-15-35.json) +completed all 81 agent runs in 2 h 55 min 24.855 s on `mac-m2-01`. Its current +deterministic audit leaves the product gate unscored: only 17 of 27 matched +VidXP/direct-local pairs were both isolated and scorable. + +On those 17 pairs, VidXP found 8 bounded chunks and direct local found 7. +VidXP averaged 194,499 tokens and 70.045 seconds, versus 263,239 tokens and +94.565 seconds. These filtered deltas cannot establish a win because the ten +excluded pairs may bias them. Separately, the saved VidXP jobs put a tIoU-0.5 +match at rank one for 6/26 jobs and within the top three for 14/26. That points +to final ranking, not candidate absence alone, as the main product limitation. +The saved agents were required to return one final clip, so this run cannot be +rescored as agent Success@3. The current isolated pilot above permits up to +three ordered clips for every condition and reports both Success@1 and +Success@3. +The [metric database](metric_database.md#first-held-out-pilot-audit) records the +full condition totals, exclusion causes, and research boundary. + +## Codex MCP development smoke + +Evaluation +[`eval-0eL-2026-09-05T22:40:10`](runs/eval-0eL-2026-09-05T22-40-10.json) +is the first corrected smoke. It uses +one neutral prompt, separate condition homes, and the practical-clip contract. + +| Condition | Result | Time | Total / uncached / output tokens | Turns / items / tools | Promptfoo cost | +| --- | --- | ---: | --- | --- | ---: | +| VidXP | `0ā€“12` s; hit `1`; coverage `1`; IoU `.500` | 72.888 s | 221,139 / 33,975 / 1,820 | 9 / 7 / 6; 5 MCP | $0.317147 | +| Direct local | `0ā€“10` s; hit `1`; coverage `1`; IoU `.600` | 93.591 s | 373,984 / 28,323 / 2,877 | 16 / 9 / 7; 7 shell | $0.755479 | +| Clean user | `0ā€“10` s; hit `1`; coverage `1`; IoU `.600` | 245.755 s | 860,165 / 47,247 / 6,518 | 30 / 31 / 26; 26 shell | $1.572180 | + +VidXP matched direct local inspection on the primary metric with 152,845 fewer +tokens and 20.703 seconds lower latency. Its saved top fused result was `0ā€“10` +seconds with action, scene, and sound support. The agent expanded the returned +clip to `0ā€“12`, so answer IoU fell from the retrieval result's `.600` to `.500`. +The clean-user agent began without third-party media tools and installed its own +workspace-local FFmpeg package. This confirms the condition works; its setup +strategy is agent behavior, not a prescribed harness path. + +All three assertions passed. The report correctly leaves the product gate +unscored because a one-task development smoke cannot establish comparative +quality. A per-run workspace reset was added afterward so repeated pilot cases +cannot inherit files or installed tools. Root-denied Codex permission profiles +and preflight filesystem probes were also added afterward, so this saved smoke +does not validate the current isolation path. + +### Historical development runs + +The runs below predate the neutral prompt and separate condition homes. Their +retrieval traces remain useful, but their condition deltas are invalid. + +Evaluation +[`eval-2uz-2026-09-05T17:39:13`](runs/eval-2uz-2026-09-05T17-39-13.json) +asked both conditions for an 8ā€“12 +second practical clip around the `0ā€“6` second rain, wind, and engine event. + +| Condition | Result | Time | Token usage | Tools | Promptfoo cost | +| --- | --- | ---: | --- | --- | ---: | +| VidXP-on | `0ā€“10` s; bounded hit `1`; coverage `1`; IoU `.6000` | 78.660 s | 200,142 total; 198,506 input; 146,048 cached; 52,458 uncached; 1,636 output; 490 reasoning | one skill read; five MCP calls; no media-shell calls | $0.384394 | +| VidXP-off | `0ā€“10` s; bounded hit `1`; coverage `1`; IoU `.6000` | 90.582 s | 277,660 total; 275,133 input; 247,296 cached; 27,837 uncached; 2,527 output; 1,100 reasoning | seven shell calls, including six FFmpeg/ffprobe calls | $0.639381 | + +The raw run recorded 77,518 fewer VidXP tokens and 11.922 seconds lower latency, +but the tool-aware prompt means those deltas are not an ablation result. The +durable job +ranked `0ā€“10` seconds first with action, scene, and sound support. This confirms +the retrieval path on one development query; it does not establish comparative +efficiency or held-out accuracy. + +The two older runs below used the superseded exact-interval prompt. Their raw +measurements are retained rather than silently rescored. + +[`eval-jJD-2026-09-01T17:51:57`](runs/eval-jJD-2026-09-01T17-51-57.json) +returned `64.031ā€“75.809` seconds with VidXP and `0ā€“6.8` through direct +inspection. It exposed the historical sound tokenization/integration defect; +the post-fix run below, not this failed run, describes later retrieval behavior. + +Evaluation +[`eval-J6s-2026-09-01T19:30:07`](runs/eval-J6s-2026-09-01T19-30-07.json) +asked the same Codex model to locate +one 0ā€“6 second rain, wind, and engine event with and without VidXP. Both runs +passed the harness contract. + +| Condition | Predicted interval | IoU | End error | Time | Total / uncached input / output tokens | Tool activity | Promptfoo cost | +| --- | --- | ---: | ---: | ---: | --- | --- | ---: | +| VidXP-on | 0ā€“8.0075 s | 0.7493 | +2.0075 s | 74.552 s | 301,712 / 48,423 / 1,769 | one skill load; six VidXP MCP calls; one non-media shell call | $0.815355 | +| VidXP-off | 0ā€“6.8 s | 0.8824 | +0.8 s | 112.209 s | 329,961 / 35,906 / 3,623 | ten shell media-inspection calls | $0.812527 | + +The VidXP run used fewer total tokens and finished faster, but Promptfoo's cost +was slightly higher because it used more uncached input. Cached input, uncached +input, and output use different rates, so total tokens alone do not determine +that estimate. Reasoning tokens are included in output tokens. Treat the dollar +value only as a within-run comparison metric, not an API invoice or measured +Codex-plan charge. + +The saved post-FineLAP-fix job confirms that retrieval found the correct +opening region: + +| Stage | Highest-ranked evidence | +| --- | --- | +| Action | 0ā€“8.0075 s, rank 1 | +| Scene | 1.001ā€“2.002 s, 2.002ā€“3.003 s, and 3.003ā€“4.004 s, ranks 1ā€“3 | +| Sound | 1.76ā€“1.92 s, 1.92ā€“2.08 s, and 2.08ā€“2.24 s, ranks 1ā€“3 | +| Fused | 0ā€“8.0075 s, rank 1 | + +The ranking failure seen in an earlier run came from the FineLAP tokenization +bug fixed by commit `343bd27`; it is not evidence about the current system. In +the saved post-fix run, the fixed eight-second action record overlaps the finer +scene and sound hits. The then-current connected-component union therefore +adopted the action record's full end time. This explains the +2.0075-second +error in that run; production fusion now uses rank-anchored direct overlap. + +The saved request used `top_k = 3`; at that revision, the application passed the +same value to each modality as retrieval depth and final output depth. That was +a separate candidate-depth limitation: a later boundary stage could not use +lower-ranked fine-grained evidence that was never retrieved. It does not by +itself explain the eight-second endpoint in this example. The retained scene +hits end at 4.004 seconds and the retained sound hits end at 2.24 seconds, so +those sparse boundaries also cannot determine the annotated 6-second end. +Paper-derived score-curve localization must be evaluated from the dense +sequence, not reconstructed from these seven retained hits. + +The full-modality probe for this task queried all 572 indexed records with one +local text-embedding call per modality. It did not invoke Codex or rerun the +Promptfoo evaluation: + +| Modality | Records | Highest-ranked interval | Best individual-record oracle | +| --- | ---: | --- | --- | +| Action | 10 | 0ā€“8.0075 s, IoU 0.7493 | 0ā€“8.0075 s, rank 1, IoU 0.7493 | +| Scene | 76 | 1.001ā€“2.002 s, IoU 0.1668 | 2.002ā€“3.003 s, rank 2, IoU 0.1668 | +| Sound | 486 | 1.76ā€“1.92 s, IoU 0.0267 | 0ā€“10 s, rank 43, IoU 0.6000 | + +Individual dense records are intentionally short, so their oracle IoU is not a +boundary prediction. Their full timelines provide the useful evidence. Scene +records remain near the top through 7.007 seconds before their scores fall; +the FineLAP activation scores have a much larger within-modality drop between +seconds 6 and 7. The opening ten-second FineLAP global record ranks 43, while +the other global windows rank 480ā€“486. Thus action, scene, and sound all rank +the correct opening region. That run's `top_k = 3` truncated the dense tail, and +interval union then let the coarse action record set the 8.0075-second endpoint. + +This one task supports a transition near seven seconds, not an exact six-second +boundary. The remaining roughly one-second difference may come from the +one-second scene sampling grid, activation timing, or annotation convention; +it must be measured across the prepared tasks rather than corrected against +this annotation. + +The original saved-ranking depth control replayed all ten collective tasks +through the then-production connected-component fusion without model or API +calls: + +| Candidates per modality | Mean top-1 IoU | R@1 at .3/.5/.7 | Board R@3 at .3/.5/.7 | Output R@10 at .3/.5/.7 | +| ---: | ---: | --- | --- | --- | +| 3 | 0.1613 | .20/.20/.10 | .30/.30/.20 | .30/.30/.20 | +| 10 | 0.1718 | .20/.20/.20 | .40/.30/.30 | .40/.40/.30 | +| 20 | 0.1699 | .20/.20/.20 | .40/.40/.40 | .50/.40/.40 | +| 100 | 0.0434 | 0/0/0 | .10/.10/0 | .20/.10/0 | +| All | 0.0530 | 0/0/0 | 0/0/0 | 0/0/0 | + +More candidates initially expose useful evidence but do not improve top-one +selection. At greater depth, adjacent records form transitive overlap chains; +the full-list top result for every task spans nearly the whole video. This +rejects both the shared input/output depth and a larger fixed replacement. +RRF can remain a ranking control only after the raw records have been converted +to bounded event proposals. + +The production correction replaces transitive components with rank-anchored +direct overlap. One hit seeds each candidate, at most one hit from each other +modality can support it, and every supporting hit must overlap the seed itself. +The same saved rankings then produced: + +| Candidates per modality | R@1 at .3/.5/.7 | R@3 at .3/.5/.7 | R@5 at .3/.5/.7 | R@10 at .3/.5/.7 | +| ---: | --- | --- | --- | --- | +| 3 | .10/.10/.10 | .30/.10/.10 | .30/.10/.10 | .30/.10/.10 | +| 20 | .10/.10/.10 | .20/.10/.10 | .20/.20/.10 | .40/.20/.10 | +| 100 | .10/.10/.10 | .30/.10/.10 | .30/.20/.10 | .30/.20/.10 | +| All | .10/.10/.10 | .30/.10/.10 | .30/.20/.10 | .30/.20/.10 | + +Additional candidates no longer create video-length results. R@5 at tIoU 0.5 +is still only `.20`: the correction preserves separate candidates but does not +repair coarse source windows or provider rankings. Product `top_k` now limits +only this final ranked list. A separate `candidate_top_k` defaults to 100 +because 100 matched exhaustive input here; that is a bounded serving decision, +not a paper-derived or universally optimal depth. + +The benchmark-only Point-to-Span ASG adaptation was then applied to the saved +curves without another model call: + +| Method | Top interval | IoU | Start error | End error | Generated spans | +| --- | --- | ---: | ---: | ---: | --- | +| Previous union | 0ā€“8.0075 s | 0.7493 | 0 s | +2.0075 s | Existing top-three hits | +| P2S ASG adaptation | 0.64ā€“6.72 s | 0.7976 | +0.64 s | +0.72 s | Sound: 1; scene/action: 0 | + +This is a concluded diagnostic, not an adopted product fix. It shows that the +published adaptive expansion can use FineLAP's dense curve, but the published +prominence threshold produced no scene or action span and the result remained +below the direct-inspection baseline's `0.8824` IoU. A full agent batch would +not resolve the remaining representation failure. It motivated the +overlapping-window control recorded next; that control is also concluded. + +The frozen overlapping-window control then reindexed the development video at +4 samples per second, retaining VideoPrism's 16-frame input and advancing by 8 +samples. This produces nominal four-second windows every two seconds: + +| Method | Action rank 1 | Fused interval | Fused IoU | Action records | +| --- | --- | --- | ---: | ---: | +| Current eight-second records | 0ā€“8.0075 s | 0ā€“8.0075 s | 0.7493 | 10 | +| Four-second, two-second-stride records | 0ā€“4.0204 s | 0ā€“8.0244 s | 0.7477 | 38 | + +The alternative's first three action hits were `0ā€“4.0204`, `2.002ā€“6.0224`, +and `4.004ā€“8.0244` seconds. The second hit closely expressed the annotated +`0ā€“6`-second endpoint, but connected-component fusion joined all three and +returned the wider interval. This experiment replaced the normal action index; +it did not retain eight-second records as a first stage or rerank the shorter +records inside them. The run took 165.094 seconds to index 38 VideoPrism +batches, used 11,929,970 index bytes, and took 0.512 seconds plus one +text-embedding call to query. Point-to-Span ASG produced no action candidate on +this curve because its strongest score is the first sample and +`scipy.signal.find_peaks` does not treat an endpoint as a peak. + +This rejects only shorter overlapping records fed unchanged into the current +union. It does not reject the finer representation: selecting or reranking its +records without transitive union remained unevaluated at this stage. + +The subsequent local comparison covered all five frozen held-out tasks that +declare action evidence. It compared the current eight-second records, the +four-second records ranked over the whole video, and a two-stage path that +kept fine records whose midpoint fell inside a top-three coarse record. The +two-stage path returned one fine record without interval union. + +| Method | Mean top-1 IoU | R@1 at 0.5 | Top-3 candidate recall at 0.5 | Full-list candidate recall at 0.5 | +| --- | ---: | ---: | ---: | ---: | +| Current eight-second records | 0.0680 | 0.00 | 0.00 | 0.20 | +| Four-second records, whole video | 0.1297 | 0.20 | 0.40 | 0.60 | +| Top-three coarse records, then four-second records | 0.1297 | 0.20 | 0.40 | 0.40 | + +Fine windows therefore improved the available candidates without producing a +reliable top result. Car-siren had a qualifying fine record at rank 13, but the +coarse top three missed its region. Engine-rev's near-target record ranked 48. +Sketch had a qualifying record at rank 3, while stir-and-cover succeeded at +rank 1 with IoU `0.6484`. A four-second record cannot represent the 15-second +signing reference; its best possible IoU was `0.2666`. + +The fine indexes contained 307 records instead of 79 across three videos. The +first run measured 1,176.264 seconds of indexing; their durable generation +manifests record 1,175.579 seconds of build time and 5,966,316 committed bytes. +The shared profile store was 133,068,596 bytes including the earlier development +video. The comparison made five new local text-embedding calls and no Codex or +API calls. It rejects the tested coarse-top-three gate as a product rule. It +does not reject overlapping records as candidate evidence; their remaining +failure is ranking and variable-duration selection. + +The next development control used the no-postprocessing ShotDetect path from +Diwan et al. PySceneDetect found three disjoint proposals. Existing SigLIP2 +scores ranked the first proposal highest. The top sound hit overlapped only +that proposal; the top action hit overlapped it and the next proposal: + +| Method | Top interval | IoU | End error | Evidence ranks | +| --- | --- | ---: | ---: | --- | +| Previous connected union | 0ā€“8.0075 s | 0.7493 | +2.0075 s | Action 1, scene 1, sound 1 | +| Direct-inspection agent | 0ā€“6.8 s | 0.8824 | +0.8 s | Agent media inspection | +| Shot proposal, scene score | 0ā€“6.7401 s | 0.8902 | +0.7401 s | Scene 1 | +| Fixed shot, VidXP RRF score | 0ā€“6.7401 s | 0.8902 | +0.7401 s | Action 1, scene 1, sound 1 | + +Detection took about `1.7` seconds, produced three proposals, reused 76 scene +records, and made no model calls or index writes. This isolates the development +failure: retrieval ranks the correct region, but connected interval union +replaces its useful endpoint with the coarse action endpoint. The result does +not yet justify a product change because a single detected shot cannot show how +the rule behaves when a relevant moment crosses multiple shots. + +The confirmed held-out comparison then evaluated tasks 3ā€“10 without Codex. +Six tasks had scene scores for a direct scene-versus-RRF comparison; the two +action-and-sound tasks were reported separately rather than given undeclared +scene evidence. + +| Method and scope | Tasks | Mean IoU | Rate at tIoU 0.3 / 0.5 / 0.7 | Mean absolute start / end error | +| --- | ---: | ---: | --- | --- | +| Previous connected union, all | 8 | 0.0418 | 0 / 0 / 0 | 59.06 / 59.05 s | +| Fixed shot with RRF, all | 8 | 0.0882 | 0.125 / 0 / 0 | 93.45 / 59.59 s | +| Best single-shot oracle, all | 8 | 0.5219 | 0.625 / 0.375 / 0.375 | 18.34 / 8.70 s | +| Scene-ranked shot, scene tasks | 6 | 0.2841 | 0.333 / 0.167 / 0.167 | 54.29 / 39.78 s | +| Fixed shot with RRF, same scene tasks | 6 | 0.1175 | 0.167 / 0 / 0 | 84.38 / 37.73 s | + +For selected outputs, the threshold rate is R@1. For the best-shot oracle, it +is candidate recall: whether any single detected shot reaches the threshold. + +RRF helped none of the six comparable tasks. It retained three scene winners, +changed two zero-IoU winners to different zero-IoU winners, and harmed one. On +`phone-ring`, the scene-ranked proposal matched the reference at IoU `0.9995`. +RRF instead selected a wrong proposal with scene rank 2 and sound rank 3, +producing IoU `0.0`; its two rank contributions outweighed the correct +proposal's scene rank 1. Both action-and-sound tasks remained at IoU `0.0`. + +The proposal oracle separates the remaining failures. Five tasks cannot reach +tIoU `0.5` with any single detected shot; three can, but ranking misses the +candidate. Four of eight RRF winners use evidence that overlaps more than one +proposal. None of the references crosses a detected boundary after a +`0.05`-second tolerance, so this slice does not test multi-shot merging. + +Preparing the saved curves took `46.744` seconds and 16 local text-embedding +calls. Detecting shots across four unique videos took about `17` seconds, with no +model calls or index writes. Peak memory was not measured. This is a local +component comparison, not an agent or Promptfoo pilot run. + +### Query wording and FineLAP stream control + +A second local control compared the unchanged full query with manually separated +modality phrases on the same eight held-out tasks. The phrases used only content +stated in each task query; they did not use timestamps, retrieved results, or +video inspection. This is a wording ceiling, not an automatic planner result. + +| Ranking check over 16 task-modality pairs | Full query | Separated phrase | +| --- | ---: | ---: | +| Target-overlapping evidence in top 3 | 7 | 8 | +| Best-boundary record in top 3 | 4 | 7 | + +Nine target-overlap ranks improved, five were unchanged, and two worsened. The +mixed result rejects query rewriting as the immediate product fix. For example, +the phone-ring sound rank improved from 22 to 1, while the stir-and-cover scene +rank fell from 1 to 41. + +FineLAP uses separate audio projectors for whole-clip retrieval and frame-level +event localization. At the time of this diagnostic, VidXP stored both outputs +in one sound collection and ranked them together. Filtering the existing index +into those published paths changed sound candidate recall: + +| Sound task | Current mixed rank | 10-second window rank | Dense activation rank | +| --- | ---: | ---: | ---: | +| Car siren | 431 | 2 | 177 | +| Engine rev | 147 | 3 | 141 | +| Phone ring | 22 | 8 | 1 | +| Drumbeat | 1,020 | 13 | 829 | + +The target entered a top-three list on three of four tasks instead of zero of +four. A short sound phrase produced the same three-task coverage when used for +both streams. This supports keeping FineLAP's clip and frame rankings separate; +it does not define how to turn both lists into one final interval. The control +made 32 local text-embedding calls in about `14` seconds, with no Codex/API +calls or index writes. + +The first 2026-09-03 correction made standard sound search return only global +clips. Evaluation `eval-mw5-2026-09-02T19:40:44` then returned `0ā€“10` seconds +for VidXP and `0ā€“6.81` seconds for direct inspection, against a `0ā€“6` reference. +VidXP identified the same event, finished 11.3% faster, used 16.5% fewer total +tokens and two fewer tool calls, and had a provider estimate of `$0.401271` +versus `$0.968155`. Its IoU nevertheless fell to `0.6000` because the agent +returned the ten-second sound envelope. + +That result rejects global-only sound output as the complete product behavior. +The replacement used three global clips as a gate, then pooled and ranked their +dense activations. On the four held-out sound tasks, the gate covered two targets +but the final top three covered none; the two surviving target activations ranked +`132` and `63`. Final sound-only mean IoU and R@1 at tIoU 0.3/0.5/0.7 were all +zero against the one accepted interval per task. + +A later source-audio audit invalidated using those four numbers as a provider +quality estimate. The phone interval is effectively silent, while the engine +phrase has several correct acoustic occurrences; for example, a later model's +top frame at 242.22 seconds lies inside LongVALE's separate +241.760ā€“243.554-second engine-rev annotation. The 25.560ā€“27.560-second reference +is distinguished by a visual clause about the driver gesturing. The exact +target-only result above remains reproducible, but it neither accepts nor +rejects the sound provider. It is an auxiliary component diagnostic and does +not block the collective paired run. ## Runtime and model generations @@ -56,7 +530,7 @@ eligible modalities, reports must show three fixed rows: |---|---| | Scene only | The existing visual retrieval output | | Speech only | The existing transcript retrieval output | -| Fixed RRF fusion | Overlap-connected intervals ranked with `rrf_v1`, `k=60` | +| Fixed RRF fusion | Rank-anchored, directly overlapping candidates ranked with `rrf_v2`, `k=60` | No fused benchmark score is reported until the same frozen dataset inputs and evaluator used by the atomic rows have been run. Generated `QueryAnswer` claims @@ -152,25 +626,14 @@ The result is a useful legacy validation baseline, not a final held-out paper result. The current two-video Qwen3 smoke establishes compatibility only; it does not supersede this score. -## Next combined benchmark - -The FineLAP environmental-sound layer is implemented but has no VidXP quality -result yet. LongVALE is the primary next experiment because it contains visual, -generic-audio, and spoken evidence in long videos. The work is ordered as follows: - -1. Complete a bounded real-media FineLAP integration smoke and record resource use. -2. Convert LongVALE event descriptions into visual, sound, and speech searches. -3. Combine those result lists using one fixed, provenance-preserving rule. -4. Return the single start/end range required by the official evaluator. -5. Process one of the nine evaluation archives to measure runtime, temporary - storage, and index growth. -6. Run the complete evaluation only if that pilot finishes cleanly. - -VidXP now indexes general sound events, but implementation is not evidence of -retrieval or boundary quality. The full LongVALE query set must remain in the -official denominator, including sound-only misses. See -[multimodal model direction](model_selection.md) for the selection evidence and -benchmark roles. +## Next approved comparison + +After explicit maintainer approval, run the paired Codex comparison against the +current collective system. Report the answer and evidence, the atomic modality +hits that formed each fused result, IoU and boundary errors, every token +category, elapsed time, estimated cost, and tool calls. The sound-only control +remains a separate diagnosis; neither its failure nor a passing replacement +would itself be a LongVALE system result. ## Sources and reproduction diff --git a/docs/benchmarking/runs/eval-0eL-2026-09-05T22-40-10.json b/docs/benchmarking/runs/eval-0eL-2026-09-05T22-40-10.json new file mode 100644 index 00000000..b5e7db7f --- /dev/null +++ b/docs/benchmarking/runs/eval-0eL-2026-09-05T22-40-10.json @@ -0,0 +1,3172 @@ +{ + "evalId": "eval-0eL-2026-09-05T22:40:10", + "results": { + "version": 3, + "timestamp": "2026-09-05T22:40:10.954Z", + "prompts": [ + { + "raw": "Locate one event in the supplied video and return one practical evidence clip.\n\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. 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Return only the requested JSON object.", + "label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return one practical evidence clip.\n\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. The clip must contain\nthe event, but it does not need to trim the event's exact boundaries. For an\nevent longer than the target, choose its most representative target-size part.\nNear the start or end of the video, shift the clip instead of shortening it.\n\nBase the result on evidence you inspect rather than the filename or query alone.\nPreserve source and evidence IDs when an evidence source returns them; otherwise\nset those fields to null. If the evidence cannot be inspected, return null start\nand end values and explain the limitation. Return only the requested JSON object.", + "id": "71293c71d00f279bbde692d4b2ae38a256df846a159c7f463338360803ef43ac", + "provider": "codex-vidxp", + "metrics": { + "score": 1, + "testPassCount": 1, + "testFailCount": 0, + "testErrorCount": 0, + "assertPassCount": 3, + "assertFailCount": 0, + "totalLatencyMs": 72888, + "tokenUsage": { + "prompt": 219319, + "completion": 1820, + "cached": 185344, + "total": 221139, + "numRequests": 1, + "completionDetails": { + "reasoning": 887, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + }, + "assertions": { + "total": 0, + "prompt": 0, + "completion": 0, + "cached": 0, + "numRequests": 0, + "completionDetails": { + "reasoning": 0, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + } + } + }, + "namedScores": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "event_coverage": 1, + "chunk_duration_in_range": 1, + "temporal_iou": 0.5, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 0, + "ablation_boundary": 1 + }, + "namedScoresCount": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "event_coverage": 1, + "chunk_duration_in_range": 1, + "temporal_iou": 1, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 1, + "ablation_boundary": 1 + }, + "namedScoreWeights": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "event_coverage": 1, + "chunk_duration_in_range": 1, + "temporal_iou": 1, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 1, + "ablation_boundary": 1 + }, + "cost": 0.317147 + } + }, + { + "raw": "Locate one event in the supplied video and return one practical evidence clip.\n\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. 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{ + "spanId": "9d5aea73ce23c59f", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648206641, + "endTime": 1788648206641.931, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'command -v python3; command -v vlc; command -v mpv; command -v mediainfo; command -v exiftool; command -v ffmpeg; command -v gst-launch-1.0; command -v sox; command -v afplay; command -v qlmanage'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "/usr/bin/python3\n/usr/bin/afplay\n/usr/bin/qlmanage\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "3f0678d837c8eb8d", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648212653, + "endTime": 1788648212732.525, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_2", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"python3 - <<'PY'\nmods=['cv2','imageio','moviepy','av','librosa','soundfile','scipy','numpy','PIL','torch','torchaudio']\nfor m in mods:\n try:\n x=__import__(m); print(m,'OK',getattr(x,'__version__',''))\n except Exception as e: print(m,'NO',type(e).__name__)\nPY\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "python3: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-Tfqr3mt6' (errno=Operation not permitted)\ncv2 NO ModuleNotFoundError\nimageio NO ModuleNotFoundError\nmoviepy NO ModuleNotFoundError\nav NO ModuleNotFoundError\nlibrosa NO ModuleNotFoundError\nsoundfile NO ModuleNotFoundError\nscipy NO ModuleNotFoundError\nnumpy NO ModuleNotFoundError\nPIL NO ModuleNotFouā€¦", + "codex.duration_ms": 79, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "aa16d545631f85fd", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648219218, + "endTime": 1788648222071.598, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_3", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'command -v swift; command -v xcrun; TMPDIR=/tmp swift --version'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 03:43:39.706 xcodebuild[79654:7347123] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-zyMUjuVe' (errno=Operation not permitted)\n2026-09-06 03:43:40.357 xcodebuild[79655:7347155] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/fā€¦", + "codex.duration_ms": 2853, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "cc097a0a3de0af25", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648231110, + "endTime": 1788648231110.892, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_4", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'command -v afconvert; command -v afinfo; ls -lh media/ZYTmgi1pAIE.mp4'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "/usr/bin/afconvert\n/usr/bin/afinfo\n-rw-r--r--@ 6 staff 11M Sep 6 02:45 media/ZYTmgi1pAIE.mp4\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "c3f78464b8c4d48d", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648235778, + "endTime": 1788648235778.6416, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_5", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'afinfo media/ZYTmgi1pAIE.mp4 | head -80; afconvert media/ZYTmgi1pAIE.mp4 tmp/video_inspect_audio.m4a -f m4af -d aac -b 64000'", + "promptfoo.span.role": "target", + "codex.exit_code": 2, + "codex.status": "failed", + "codex.output": "File: media/ZYTmgi1pAIE.mp4\nFile type ID: mp4f\nNum Tracks: 1\n----\nData format: 2 ch, 44100 Hz, aac (0x00000000) 0 bits/channel, 0 bytes/packet, 1024 frames/packet, 0 bytes/frame\n no channel layout.\nestimated duration: 75.765261 sec\naudio bytes: 1213480\naudio packets: 3265\nbit rate: 128049 bits per second\npacket size upper bound: 507\nmaximum packet size: 507\naudā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 2" + }, + { + "spanId": "fcd1090fa94bd7da", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648243873, + "endTime": 1788648243873.9075, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_6", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'afconvert -hf | rg -n \"AAC|m4a|MPEG|WAVE\" | head -30'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "zsh:1: command not found: rg\nAudio file and data formats:\n '3gpp' = 3GP Audio (.3gp, .3gpp)\n data_formats: \n '3gp2' = 3GPP-2 Audio (.3g2, .3gp2)\n data_formats: \n 'adts' = AAC ADTS (.aac, .adts)\n data_formats: \n 'ac-3' = AC3 (.ac3)\n data_formats: 'ac-3' \n 'AIFC' = AIFC (.aifc, .aiff, .aif)\n data_formats: I8 BEI16 Bā€¦", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "65718e610fd9b387", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648249064, + "endTime": 1788648249064.6091, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_7", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'afconvert media/ZYTmgi1pAIE.mp4 tmp/video_inspect_audio.m4a -f m4af -b 64000; ls -lh tmp/video_inspect_audio.m4a; afinfo tmp/video_inspect_audio.m4a | head -20'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Error: ExtAudioFileCreateWithURL failed ('fmt?')\nls: tmp/video_inspect_audio.m4a: No such file or directory\nFail: AudioFileOpenURL failed\nFile: tmp/video_inspect_audio.m4a\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "03eae4d248ccbe89", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648253746, + "endTime": 1788648253746.676, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_8", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'afconvert media/ZYTmgi1pAIE.mp4 tmp/video_inspect_audio.wav -f WAVE -d LEI16@16000 -c 1; ls -lh tmp/video_inspect_audio.wav; afinfo tmp/video_inspect_audio.wav | head -20'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Error: ExtAudioFileSetProperty ('cfmt') failed ('fmt?')\nls: tmp/video_inspect_audio.wav: No such file or directory\nFail: AudioFileOpenURL failed\nFile: tmp/video_inspect_audio.wav\n", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "d31edd3066cda1fa", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648258925, + "endTime": 1788648258925.719, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_9", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'afconvert media/ZYTmgi1pAIE.mp4 tmp/video_inspect_audio.wav -f WAVE -d LEI16; ls -lh tmp/video_inspect_audio.wav'", + "promptfoo.span.role": "target", + "codex.exit_code": 1, + "codex.status": "failed", + "codex.output": "Error: ExtAudioFileSetProperty ('cfmt') failed ('fmt?')\nls: tmp/video_inspect_audio.wav: No such file or directory\n", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 1" + }, + { + "spanId": "40b171ea56203ae0", + "parentSpanId": "401d225cca201a5a", + "name": "file add", + "startTime": 1788648267745, + "endTime": 1788648267802.2073, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_10", + "codex.item.type": "file_change", + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.status": "completed", + "codex.files_changed": 1, + "codex.files": "/.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/tmp/export_audio.swift", + "codex.duration_ms": 57, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "7ecdfcd04ee1fecc", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648271989, + "endTime": 1788648277768.5906, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_11", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p /tmp/swift-module-cache && swift -module-cache-path /tmp/swift-module-cache tmp/export_audio.swift media/ZYTmgi1pAIE.mp4 tmp/video_inspect_audio.m4a && ls -lh tmp/video_inspect_audio.m4a'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 03:44:32.006 xcodebuild[79714:7347527] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-4n6jjf1f' (errno=Operation not permitted)\n2026-09-06 03:44:32.548 xcodebuild[79715:7347603] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/fā€¦", + "codex.duration_ms": 5780, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "fbbab397efb476b5", + "parentSpanId": "401d225cca201a5a", + "name": "file update", + "startTime": 1788648282552, + "endTime": 1788648282598.5005, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_12", + "codex.item.type": "file_change", + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.status": "completed", + "codex.files_changed": 1, + "codex.files": "/.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/tmp/export_audio.swift", + "codex.duration_ms": 47, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "46940ea543bf29d5", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648286500, + "endTime": 1788648288964.821, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_13", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'rm tmp/video_inspect_audio.m4a; swift -module-cache-path /tmp/swift-module-cache tmp/export_audio.swift media/ZYTmgi1pAIE.mp4 tmp/video_inspect_audio.m4a; ls -lh tmp/video_inspect_audio.m4a'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 03:44:46.566 xcodebuild[79776:7347947] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-UYmhmn8f' (errno=Operation not permitted)\n2026-09-06 03:44:47.088 xcodebuild[79777:7347979] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/fā€¦", + "codex.duration_ms": 2465, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "b381f96db6d7ac8d", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648293893, + "endTime": 1788648293945.2573, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_14", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'base64 < tmp/video_inspect_audio.m4a'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "AAAAHGZ0eXBNNEEgAAAAAE00QSBpc29tbXA0MgAAAAFtZGF0AAAAAAAShDghAAUAoBv/wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAā€¦", + "codex.duration_ms": 25, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "f2ade3effbbdf97f", + "parentSpanId": "401d225cca201a5a", + "name": "file update", + "startTime": 1788648304609, + "endTime": 1788648304657.4795, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_15", + "codex.item.type": "file_change", + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.status": "completed", + "codex.files_changed": 1, + "codex.files": "/.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/tmp/export_audio.swift", + "codex.duration_ms": 49, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "144dc83a30c6d3be", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648310131, + "endTime": 1788648323689.1965, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_16", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p tmp/audio_segments; for n in 0 1 2 3 4 5; do s=$((n*12)); swift -module-cache-path /tmp/swift-module-cache tmp/export_audio.swift media/ZYTmgi1pAIE.mp4 tmp/audio_segments/seg${n}.m4a $s 13 >/dev/null 2>&1; done; ls -lh tmp/audio_segments'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "total 2496\n-rw-r--r--@ 1 staff 207K Sep 6 03:45 seg0.m4a\n-rw-r--r--@ 1 staff 207K Sep 6 03:45 seg1.m4a\n-rw-r--r--@ 1 staff 207K Sep 6 03:45 seg2.m4a\n-rw-r--r--@ 1 staff 206K Sep 6 03:45 seg3.m4a\n-rw-r--r--@ 1 staff 207K Sep 6 03:45 seg4.m4a\n-rw-r--r--@ 1 staff 207K Sep 6 03:45 seg5.m4a\n", + "codex.duration_ms": 13558, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "d883a3f83cf11ba9", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648329068, + "endTime": 1788648329085.9563, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_17", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'base64 < tmp/audio_segments/seg0.m4a'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": 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fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-gnQMHNC1' (errno=Operation not permitted)\n2026-09-06 03:45:44.615 xcodebuild[79939:7349224] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/fā€¦", + "codex.duration_ms": 3091, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "377160c0223dca5c", + "parentSpanId": "401d225cca201a5a", + "name": "exec /bin/zsh", + "startTime": 1788648355596, + "endTime": 1788648355596.9402, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_25", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"mkdir -p tmp/ql; qlmanage -t -s 1000 -o tmp/ql media/ZYTmgi1pAIE.mp4 >/tmp/ql.log 2>&1; ls -lh tmp/ql; sed -n '1,80p' /tmp/ql.log\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": 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temporal IoU 0.6000." + }, + "statusCode": 1 + }, + { + "spanId": "0072c7f6cdef64a6", + "name": "promptfoo.test_case", + "startTime": 1788648178883, + "endTime": 1788648425089.2854, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-0eL-2026-09-05T22:40:10", + "promptfoo.test.index": 2, + "promptfoo.test_case.id": "2-2", + "promptfoo.prompt.index": 2, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return one practical evidence clip.\n\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + } + ] +} diff --git a/docs/benchmarking/runs/eval-2uz-2026-09-05T17-39-13.json b/docs/benchmarking/runs/eval-2uz-2026-09-05T17-39-13.json new file mode 100644 index 00000000..6bcedb6d --- /dev/null +++ b/docs/benchmarking/runs/eval-2uz-2026-09-05T17-39-13.json @@ -0,0 +1,1803 @@ +{ + "evalId": "eval-2uz-2026-09-05T17:39:13", + "results": { + "version": 3, + "timestamp": "2026-09-05T17:39:13.436Z", + "prompts": [ + { + "raw": "Locate one event in the supplied video and return one practical evidence clip.\n\nDataset: {{ dataset }}\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. 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Return only the requested JSON object.", + "label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return one practical evidence clip.\n\nDataset: {{ dataset }}\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. The clip must contain\nthe event, but it does not need to trim the event's exact boundaries. For an\nevent longer than the target, choose its most representative target-size part.\nNear the start or end of the video, shift the clip instead of shortening it.\n\nUse VidXP when it is available in this condition; otherwise use the local media\nand available read-only tools. Do not use the network, read benchmark\nannotations, or invoke the VidXP CLI from the shell. 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Return only the requested JSON object.", + "id": "0cde417e8514ba3c2e189ed30d474dc7238ef9b4e4654ed296979ab9ec11a1f5", + "provider": "codex-vidxp", + "metrics": { + "score": 1, + "testPassCount": 1, + "testFailCount": 0, + "testErrorCount": 0, + "assertPassCount": 3, + "assertFailCount": 0, + "totalLatencyMs": 78660, + "tokenUsage": { + "prompt": 198506, + "completion": 1636, + "cached": 146048, + "total": 200142, + "numRequests": 1, + "completionDetails": { + "reasoning": 490, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + }, + "assertions": { + "total": 0, + "prompt": 0, + "completion": 0, + "cached": 0, + "numRequests": 0, + "completionDetails": { + "reasoning": 0, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + } + } + }, + "namedScores": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "event_coverage": 1, + "chunk_duration_in_range": 1, + "temporal_iou": 0.6, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 0, + "ablation_boundary": 1 + }, + "namedScoresCount": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "event_coverage": 1, + "chunk_duration_in_range": 1, + "temporal_iou": 1, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 1, + "ablation_boundary": 1 + }, + "namedScoreWeights": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "event_coverage": 1, + "chunk_duration_in_range": 1, + "temporal_iou": 1, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 1, + "ablation_boundary": 1 + }, + "cost": 0.384394 + } + }, + { + "raw": "Locate one event in the supplied video and return one practical evidence clip.\n\nDataset: {{ dataset }}\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. 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Return only the requested JSON object.", + "config": {} + }, + "promptId": "0cde417e8514ba3c2e189ed30d474dc7238ef9b4e4654ed296979ab9ec11a1f5", + "promptIdx": 0, + "traceId": "2ba59a24e277f787d7a3aa3c45baece0", + "evaluationId": "eval-2uz-2026-09-05T17:39:13", + "provider": { + "id": "openai:codex-sdk", + "label": "codex-vidxp" + }, + "response": { + "output": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"Use 0.000ā€“10.000 seconds: the clip captures the storm-lashed desolate landscape and the transition into the engine starting and revving.\",\"start_seconds\":0,\"end_seconds\":10,\"modalities\":[\"scene\",\"action\",\"sound\"],\"source_job_id\":\"b1313834e71b43dbadbc2cf8af38a7e7\",\"evidence\":[{\"evidence_id\":\"b5100a0dc6d70d6e047b3ce96bcf719eeb6f27ae00b25d05ba186b11feff1807\",\"start_seconds\":0,\"end_seconds\":10,\"modality\":\"sound\",\"description\":\"Top-ranked multimodal VidXP evidence: heavy rain and howling wind over a barren landscape, followed by an engine starting and 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{{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ ā€¦", + "promptfoo.request.body": "Locate one event in the supplied video and return one practical evidence clip.\n\nDataset: LongVALE evaluation\nVideo ID: ZYTmgi1pAIE\nMedia path: media/ZYTmgi1pAIE.mp4\nVideo duration: 75.809067 seconds\nEvent to locate: heavy rain and howling wind over a desolate landscape, followed by an engine starting and revving\nEvidence clip: aim for 10 seconds and keep it between\n8 and 12 seconds. The clip must ā€¦", + "gen_ai.usage.input_tokens": 275133, + "gen_ai.usage.output_tokens": 2527, + "promptfoo.usage.total_tokens": 277660, + "gen_ai.usage.cache_read.input_tokens": 247296, + "gen_ai.usage.reasoning.output_tokens": 1100, + "gen_ai.response.model": "gpt-5.6-sol", + "gen_ai.response.id": "01a072a8-62ab-75b3-a159-f0fb7f5c50ce", + "promptfoo.response.body": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"Heavy rain and howling wind play over a barren, desolate landscape at the opening, followed by an engine starting and revving during the same intro sequence.\",\"start_seconds\":0,\"end_seconds\":10,\"modalities\":[\"scene\",\"sound\"],\"source_job_id\":null,\"evidence\":[{\"evidence_id\":null,\"start_seconds\":0,\"end_seconds\":10,\"modality\":\"sound\",\"description\":\"The opening 10-seā€¦", + "codex.conversation.message_count": 2, + "codex.items.total": 8, + "codex.items.breakdown": "{\"command_execution\":7,\"agent_message\":1}" + }, + "statusCode": 1 + }, + { + "spanId": "ee4d245ca709f5c1", + "parentSpanId": "3e3de295aaf6ef67", + "name": "codex-baseline", + "startTime": 1788630033035, + "endTime": 1788630123616.0996, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.span.role": "target", + "promptfoo.target.type": "provider", + "promptfoo.target.label": "codex-baseline", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return one practical evidence clip.\n\nDataset: {{ dataset }}\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ ā€¦", + "promptfoo.eval.id": "eval-2uz-2026-09-05T17:39:13", + "promptfoo.test.index": 1 + }, + "statusCode": 1 + }, + { + "spanId": "3abdce2ccbce7ecc", + "parentSpanId": "3e3de295aaf6ef67", + "name": "grader is-json", + "startTime": 1788630123890, + "endTime": 1788630123890.737, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-2uz-2026-09-05T17:39:13", + "promptfoo.test.index": 1, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "6b89bd35565a11ec", + "parentSpanId": "3e3de295aaf6ef67", + "name": "grader python", + "startTime": 1788630123890, + "endTime": 1788630124009.001, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-2uz-2026-09-05T17:39:13", + "promptfoo.test.index": 1, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Bounded chunk hit: 10.000s duration, 1.0000 event coverage; temporal IoU 0.6000." + }, + "statusCode": 1 + }, + { + "spanId": "38e10e0a6b615a0b", + "parentSpanId": "3e3de295aaf6ef67", + "name": "grader python", + "startTime": 1788630123890, + "endTime": 1788630124009.2207, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-2uz-2026-09-05T17:39:13", + "promptfoo.test.index": 1, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "VidXP-off remained isolated from the skill, MCP, and CLI." + }, + "statusCode": 1 + }, + { + "spanId": "3e3de295aaf6ef67", + "name": "promptfoo.test_case", + "startTime": 1788630033034, + "endTime": 1788630124009.8044, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-2uz-2026-09-05T17:39:13", + "promptfoo.test.index": 1, + "promptfoo.test_case.id": "1-1", + "promptfoo.prompt.index": 1, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return one practical evidence clip.\n\nDataset: {{ dataset }}\nVideo ID: {{ video_id }}\nMedia path: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEvidence clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ ā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + } + ] +} diff --git a/docs/benchmarking/runs/eval-7VR-2026-09-06T10-58-07.json b/docs/benchmarking/runs/eval-7VR-2026-09-06T10-58-07.json new file mode 100644 index 00000000..6d07173a --- /dev/null +++ b/docs/benchmarking/runs/eval-7VR-2026-09-06T10-58-07.json @@ -0,0 +1,62984 @@ +{ + "evalId": "eval-7VR-2026-09-06T10:58:07", + "results": { + "version": 3, + "timestamp": "2026-09-06T10:58:07.499Z", + "prompts": [ + { + "raw": "Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEach clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. The clip must contain\nthe event, but it does not need to trim the event's exact boundaries. For an\nevent longer than the target, choose its most representative target-size part.\nNear the start or end of the video, shift the clip instead of shortening it.\n\nGround each candidate in available evidence rather than the filename or query\nalone. Reuse evidence already returned by a tool; do not perform extra inspection\nsolely to reconfirm an already supported candidate. Preserve the source job and\ncandidate evidence IDs when an evidence source returns them; otherwise use an\nempty evidence-ID list and set the source job to null.\nIf no candidate can be grounded, explain the limitation in the answer and return\nan empty candidate list. Return only the requested JSON object.", + "label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEach clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. The clip must contain\nthe event, but it does not need to trim the event's exact boundaries. 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Return only the requested JSON object.", + "id": "65a90d5c131803b563ac8d08cdff008ac84dcbf8b4d860048b611fbf1d628ebd", + "provider": "codex-clean-user", + "metrics": { + "score": 20.34125592185592, + "testPassCount": 9, + "testFailCount": 18, + "testErrorCount": 0, + "assertPassCount": 60, + "assertFailCount": 21, + "totalLatencyMs": 6681035, + "tokenUsage": { + "prompt": 18686536, + "completion": 136228, + "cached": 17343104, + "total": 18822764, + "numRequests": 27, + "completionDetails": { + "reasoning": 46657, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + }, + "assertions": { + "total": 0, + "prompt": 0, + "completion": 0, + "cached": 0, + "numRequests": 0, + "completionDetails": { + "reasoning": 0, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + } + } + }, + "namedScores": { + "temporal_grounding": 17.023767765567765, + "valid_interval": 27, + "bounded_chunk_hit": 16, + "bounded_chunk_hit_at_1": 16, + "bounded_chunk_hit_at_3": 16, + "bounded_chunk_mrr": 16, + "candidate_count": 29, + "event_coverage": 17.023767765567765, + "top1_event_coverage": 17.023767765567765, + "chunk_duration_in_range": 27, + "candidate_duration_in_range_rate": 27, + "temporal_iou": 6.475384955420501, + "best_temporal_iou": 6.475384955420501, + "r1_tiou_0_3": 11, + "r1_tiou_0_5": 6, + "r1_tiou_0_7": 0, + "r3_tiou_0_3": 11, + "r3_tiou_0_5": 6, + "r3_tiou_0_7": 0, + "ablation_boundary": 17 + }, + "namedScoresCount": { + "temporal_grounding": 27, + "valid_interval": 27, + "bounded_chunk_hit": 27, + "bounded_chunk_hit_at_1": 27, + "bounded_chunk_hit_at_3": 27, + "bounded_chunk_mrr": 27, + "candidate_count": 27, + "event_coverage": 27, + "top1_event_coverage": 27, + "chunk_duration_in_range": 27, + "candidate_duration_in_range_rate": 27, + "temporal_iou": 27, + "best_temporal_iou": 27, + "r1_tiou_0_3": 27, + "r1_tiou_0_5": 27, + "r1_tiou_0_7": 27, + "r3_tiou_0_3": 27, + "r3_tiou_0_5": 27, + "r3_tiou_0_7": 27, + "ablation_boundary": 27 + }, + "namedScoreWeights": { + "temporal_grounding": 27, + "valid_interval": 27, + "bounded_chunk_hit": 27, + "bounded_chunk_hit_at_1": 27, + "bounded_chunk_hit_at_3": 27, + "bounded_chunk_mrr": 27, + "candidate_count": 27, + "event_coverage": 27, + "top1_event_coverage": 27, + "chunk_duration_in_range": 27, + "candidate_duration_in_range_rate": 27, + "temporal_iou": 27, + "best_temporal_iou": 27, + "r1_tiou_0_3": 27, + "r1_tiou_0_5": 27, + "r1_tiou_0_7": 27, + "r3_tiou_0_3": 27, + "r3_tiou_0_5": 27, + "r3_tiou_0_7": 27, + "ablation_boundary": 27 + }, + "cost": 36.348442000000006 + } + } + ], + "results": [ + { + "cost": 1.009237, + "gradingResult": { + "pass": true, + "score": 1, + "reason": "All assertions passed", + "namedScores": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "bounded_chunk_hit_at_1": 1, + "bounded_chunk_hit_at_3": 1, + "bounded_chunk_mrr": 1, + "candidate_count": 1, + "event_coverage": 1, + "top1_event_coverage": 1, + "chunk_duration_in_range": 1, + "candidate_duration_in_range_rate": 1, + "temporal_iou": 0.5672000000000011, + "best_temporal_iou": 0.5672000000000011, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 0, + "r3_tiou_0_3": 1, + "r3_tiou_0_5": 1, + "r3_tiou_0_7": 0, + "ablation_boundary": 1 + }, + "namedScoreWeights": { + "temporal_grounding": 1, + "valid_interval": 1, + "bounded_chunk_hit": 1, + "bounded_chunk_hit_at_1": 1, + "bounded_chunk_hit_at_3": 1, + "bounded_chunk_mrr": 1, + "candidate_count": 1, + "event_coverage": 1, + "top1_event_coverage": 1, + "chunk_duration_in_range": 1, + "candidate_duration_in_range_rate": 1, + "temporal_iou": 1, + "best_temporal_iou": 1, + "r1_tiou_0_3": 1, + "r1_tiou_0_5": 1, + "r1_tiou_0_7": 1, + "r3_tiou_0_3": 1, + "r3_tiou_0_5": 1, + "r3_tiou_0_7": 1, + "ablation_boundary": 1 + }, + "tokensUsed": { + "total": 0, + "prompt": 0, + "completion": 0, + "cached": 0, + "numRequests": 0 + }, + "componentResults": [ + { + "pass": true, + "score": 1, + "reason": "Assertion passed", + "assertion": { + "type": "is-json" + } + }, + { + "pass": true, + "score": 1, + "reason": "Bounded chunk hit in 1 candidate(s); 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: ZYTmgi1pAIE\nLocal media path, when available: media/ZYTmgi1pAIE.mp4\nVideo duration: 75.813152 seconds\nEvent to locate: a ringing bell echoes while SUBSCRIBE flashes over a starry night sky\nEach clip: aim for 10 seconds and keep it between\n8 and 12 seconds. The clip must contain\nthe event, but it does not need to trim the event's exact boundaries. For an\nevent longer than the target, choose its most representative target-size part.\nNear the start or end of the video, shift the clip instead of shortening it.\n\nGround each candidate in available evidence rather than the filename or query\nalone. Reuse evidence already returned by a tool; do not perform extra inspection\nsolely to reconfirm an already supported candidate. Preserve the source job and\ncandidate evidence IDs when an evidence source returns them; otherwise use an\nempty evidence-ID list and set the source job to null.\nIf no candidate can be grounded, explain the limitation in the answer and return\nan empty candidate list. Return only the requested JSON object.", + "label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locate: {{ query }}\nEach clip: aim for {{ target_chunk_seconds }} seconds and keep it between\n{{ min_chunk_seconds }} and {{ max_chunk_seconds }} seconds. The clip must contain\nthe event, but it does not need to trim the event's exact boundaries. For an\nevent longer than the target, choose its most representative target-size part.\nNear the start or end of the video, shift the clip instead of shortening it.\n\nGround each candidate in available evidence rather than the filename or query\nalone. Reuse evidence already returned by a tool; do not perform extra inspection\nsolely to reconfirm an already supported candidate. Preserve the source job and\ncandidate evidence IDs when an evidence source returns them; otherwise use an\nempty evidence-ID list and set the source job to null.\nIf no candidate can be grounded, explain the limitation in the answer and return\nan empty candidate list. Return only the requested JSON object.", + "config": {} + }, + "promptId": "65a90d5c131803b563ac8d08cdff008ac84dcbf8b4d860048b611fbf1d628ebd", + "promptIdx": 0, + "traceId": "529a6829e26cb9ee19873952d3799851", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "provider": { + "id": "openai:codex-sdk", + "label": "codex-vidxp" + }, + "response": { + "output": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The event is best supported near the end of the video, around 70.0ā€“75.813 seconds.\",\"source_job_id\":\"b26aee15c4c349aa9359fb0514489291\",\"candidates\":[{\"start_seconds\":65.813152,\"end_seconds\":75.813152,\"modalities\":[\"scene\",\"action\",\"sound\"],\"description\":\"A 10-second end-shifted clip containing the starry SUBSCRIBE animation and ringing-bell audio; evidence contributors cover sound from 70.0 seconds, action from 72.005 seconds, and the visual scene around 73.006ā€“74.007 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"evaluation_mode": "pilot", + "repetition": 1, + "tracingEnabled": true, + "evaluationId": "eval-7VR-2026-09-06T10:58:07" + }, + "assert": [ + { + "type": "is-json" + }, + { + "type": "python", + "value": "file://../../src/vidxp/benchmarks/agent_ablation_score.py:score_temporal_grounding", + "metric": "temporal_grounding" + }, + { + "type": "python", + "value": "file://../../src/vidxp/benchmarks/agent_ablation_score.py:score_ablation_boundary", + "metric": "ablation_boundary" + } + ], + "options": {} + }, + "testIdx": 0, + "tokenUsage": { + "prompt": 379516, + "completion": 2065, + "cached": 319872, + "total": 381581, + "numRequests": 1, + "completionDetails": { + "reasoning": 914, + "acceptedPrediction": 0, + "rejectedPrediction": 0, + "cacheReadInputTokens": 0, + "cacheCreationInputTokens": 0 + }, + "assertions": { + "total": 0, + "prompt": 0, + "completion": 0, + "cached": 0, + "numRequests": 0, + "completionDetails": { + "reasoning": 0, + "acceptedPrediction": 0, + 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.request.body": "Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 0 + }, + "statusCode": 1 + }, + { + "spanId": "15034074e881c129", + "parentSpanId": "01c3968c47967a58", + "name": "grader is-json", + "startTime": 1788692391502, + "endTime": 1788692391504.4639, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 0, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "c6058277c0f2d9dc", + "parentSpanId": "01c3968c47967a58", + "name": "grader python", + "startTime": 1788692391504, + "endTime": 1788692391611.9258, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 0, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Bounded chunk hit in 1 candidate(s); top-1 hit, first hit rank 1, best coverage 1.0000, best temporal IoU 0.5672." + }, + "statusCode": 1 + }, + { + "spanId": "f5498be7f1e05188", + "parentSpanId": "01c3968c47967a58", + "name": "grader python", + "startTime": 1788692391504, + "endTime": 1788692392225.856, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 0, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "VidXP-on returned evidence from a fresh, successful, matching MCP job." + }, + "statusCode": 1 + }, + { + "spanId": "01c3968c47967a58", + "name": "promptfoo.test_case", + "startTime": 1788692287529, + "endTime": 1788692392225.5596, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 0, + "promptfoo.test_case.id": "0-0", + "promptfoo.prompt.index": 0, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + }, + { + "traceId": "8ce2ff3e50bb40e36bfc236f3a452202", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "1-1", + "metadata": { + "testIdx": 1, + "promptIdx": 1, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZYT-bell-subscribe", + "dataset": "LongVALE evaluation", + "video_id": "ZYTmgi1pAIE", + "media_relpath": "media/ZYTmgi1pAIE.mp4", + "duration_seconds": 75.813152, + "event_index": 2, + "query": "a ringing bell echoes while SUBSCRIBE flashes over a starry night sky", + "expected_start": 70.07, + "expected_end": 75.742, + "modalities": "[\"scene\",\"sound\"]", + 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"statusCode": 1 + }, + { + "spanId": "1d0acce826efa8b1", + "parentSpanId": "47e22fddf99be986", + "name": "exec /bin/zsh", + "startTime": 1788692408675, + "endTime": 1788692408675.6785, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"ffmpeg -hide_banner -i media/ZYTmgi1pAIE.mp4 -vf \\\"fps=1/3,scale=320:-1,tile=5x6:padding=4:margin=4,drawtext=text='%{pts\\\\:hms}':x=8:y=h-24:fontsize=16:fontcolor=white:box=1:boxcolor=black@0.6\\\" -frames:v 1 contact.jpg\"", + "promptfoo.span.role": "target", + "codex.exit_code": 8, + "codex.status": "failed", + "codex.output": "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'media/ZYTmgi1pAIE.mp4':\n Metadata:\n major_brand : mp42\n minor_version : 0\n compatible_brands: isommp42\n creation_time : 2024-03-16T00:08:58.000000Z\n Duration: 00:01:15.81, start: 0.000000, bitrate: 1165 kb/s\n Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 10ā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 8" + }, + { + "spanId": "86d4277a2b03f516", + "parentSpanId": "47e22fddf99be986", + "name": "exec /bin/zsh", + "startTime": 1788692416016, + "endTime": 1788692416537.7952, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_2", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \"fps=1/3,scale=320:-1,tile=5x6:padding=4:margin=4\" -frames:v 1 contact.jpg'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + "codex.duration_ms": 522, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": 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"codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"ffmpeg -hide_banner -loglevel error -ss 64 -i media/ZYTmgi1pAIE.mp4 -t 11.8 -vn -ac 1 -c:a libopus -b:a 24k end_audio.ogg && base64 -i end_audio.ogg | tr -d '\\\\n'\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "[REDACTED]", + "codex.duration_ms": 40, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "7b90cad3431005c6", + "parentSpanId": "47e22fddf99be986", + "name": "agent response", + "startTime": 1788692442652, + "endTime": 1788692460080, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_5", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The event occurs in the closing end-card sequence: the scene changes to a starry night sky around 70 seconds, then ā€œSUBSCRIBE!ā€ flashes as a ringing bell reverberates. One grounded candidate is sufficient because the event occurs only once.\",\"source_job_id\":null,\"candidates\":[{\"start_seconds\":65.813,\"end_seconds\":75.813,\"modalities\":[\"scene\",\"action\",\"sound\"],\"dā€¦", + "codex.duration_ms": 17427, + "codex.had_start_event": false + }, + "statusCode": 1 + }, + { + "spanId": "72123b9851fa61b4", + "parentSpanId": "47e22fddf99be986", + "name": "gen_ai.turn 1", + "startTime": 1788692393470, + "endTime": 1788692460096, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "gen_ai.turn.index": 1, + "gen_ai.provider.name": "openai", + "promptfoo.span.role": "target", + "gen_ai.usage.input_tokens": 182923, + "gen_ai.usage.output_tokens": 1845, + "gen_ai.usage.cache_read.input_tokens": 151040, + "gen_ai.usage.reasoning.output_tokens": 890 + }, + "statusCode": 1 + }, + { + "spanId": "47e22fddf99be986", + "parentSpanId": "d46b29ef7d0c7961", + "name": "invoke_agent Codex", + "startTime": 1788692392258, + "endTime": 1788692461827.958, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "gen_ai.provider.name": "openai", + "gen_ai.operation.name": "invoke_agent", + "promptfoo.provider.id": "openai:codex-sdk", + "gen_ai.agent.name": "Codex", + "gen_ai.request.model": "gpt-5.6-sol", + "promptfoo.span.role": "target", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.request.body": "Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: ZYTmgi1pAIE\nLocal media path, when available: media/ZYTmgi1pAIE.mp4\nVideo duration: 75.813152 seconds\nEvent to locate: a ringing bell echoes while SUBSCRIBE flashes over a starry nightā€¦", + "gen_ai.usage.input_tokens": 182923, + "gen_ai.usage.output_tokens": 1845, + "promptfoo.usage.total_tokens": 184768, + "gen_ai.usage.cache_read.input_tokens": 151040, + "gen_ai.usage.reasoning.output_tokens": 890, + "gen_ai.response.model": "gpt-5.6-sol", + "gen_ai.response.id": "01a0765f-ed44-7620-9bea-9e7f6d76af7d", + "promptfoo.response.body": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The event occurs in the closing end-card sequence: the scene changes to a starry night sky around 70 seconds, then ā€œSUBSCRIBE!ā€ flashes as a ringing bell reverberates. One grounded candidate is sufficient because the event occurs only once.\",\"source_job_id\":null,\"candidates\":[{\"start_seconds\":65.813,\"end_seconds\":75.813,\"modalities\":[\"scene\",\"action\",\"sound\"],\"dā€¦", + "codex.conversation.message_count": 3, + "codex.items.total": 6, + "codex.items.breakdown": "{\"agent_message\":2,\"command_execution\":4}" + }, + "statusCode": 1 + }, + { + "spanId": "d46b29ef7d0c7961", + "parentSpanId": "be2cba60adf7c8f1", + "name": "codex-baseline", + "startTime": 1788692392254, + "endTime": 1788692461828.1052, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.span.role": "target", + "promptfoo.target.type": "provider", + "promptfoo.target.label": "codex-baseline", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 1 + }, + "statusCode": 1 + }, + { + "spanId": "eae9e9a43740b53d", + "parentSpanId": "be2cba60adf7c8f1", + "name": "grader is-json", + "startTime": 1788692462098, + "endTime": 1788692462098.888, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 1, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "0f39c3a58af90765", + "parentSpanId": "be2cba60adf7c8f1", + "name": "grader python", + "startTime": 1788692462099, + "endTime": 1788692462197.408, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 1, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Bounded chunk hit in 1 candidate(s); top-1 hit, first hit rank 1, best coverage 1.0000, best temporal IoU 0.5672." + }, + "statusCode": 1 + }, + { + "spanId": "ba81fa7ee618c9ac", + "parentSpanId": "be2cba60adf7c8f1", + "name": "grader python", + "startTime": 1788692462099, + "endTime": 1788692462198.1267, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 1, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "The condition remained isolated from VidXP and respected its tool policy." + }, + "statusCode": 1 + }, + { + "spanId": "be2cba60adf7c8f1", + "name": "promptfoo.test_case", + "startTime": 1788692392252, + "endTime": 1788692462197.0276, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 1, + "promptfoo.test_case.id": "1-1", + "promptfoo.prompt.index": 1, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + }, + { + "traceId": "f570896e12d0b791a69b4e828f13f876", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "2-2", + "metadata": { + "testIdx": 2, + "promptIdx": 2, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZYT-bell-subscribe", + "dataset": "LongVALE evaluation", + "video_id": "ZYTmgi1pAIE", + "media_relpath": "media/ZYTmgi1pAIE.mp4", + "duration_seconds": 75.813152, + "event_index": 2, + "query": "a ringing bell echoes while SUBSCRIBE flashes over a starry night sky", + "expected_start": 70.07, + "expected_end": 75.742, + "modalities": "[\"scene\",\"sound\"]", + "condition": "clean-user", + "expected_vidxp": false, + "allow_media_shell": true, + "forbid_host_tools": true, + "evaluation_mode": "pilot", + "repetition": 1, + "target_chunk_seconds": 10, + "min_chunk_seconds": 8, + "max_chunk_seconds": 12, + "min_event_coverage": 0.5, + "max_candidates": 3 + } + }, + "spans": [ + { + "spanId": "851c9226c1f4e5a2", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692530110, + "endTime": 1788692530110.7905, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_0", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'ls -lh media/ZYTmgi1pAIE.mp4 && ffprobe -v error -show_entries format=duration -show_entries stream=index,codec_type,codec_name -of json media/ZYTmgi1pAIE.mp4'", + "promptfoo.span.role": "target", + "codex.exit_code": 127, + "codex.status": "failed", + "codex.output": "-rw-r--r--@ 5 staff 11M Sep 6 15:40 media/ZYTmgi1pAIE.mp4\nzsh:1: command not found: ffprobe\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 127" + }, + { + "spanId": "d109cb2bb145e343", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692546208, + "endTime": 1788692549816.1538, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"python3 - <<'PY'\nmods=['cv2','av','moviepy','imageio_ffmpeg','librosa','soundfile','pydub','torch','whisper']\nfor m in mods:\n try:\n mod=__import__(m)\n print(m,'OK',getattr(mod,'__version__',''))\n except Exception as e: print(m,'NO',type(e).__name__)\nPY\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 16:02:26.764 xcodebuild[30655:9207077] DVTFilePathFSEvents: Failed to start fs event stream.\npython3: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-FSc9SOY7' (errno=Operation not permitted)\n2026-09-06 16:02:28.483 xcodebuild[30659:9207109] DVTFilePathFSEvents: Failed to start fs event stream.\ncv2 NO ModuleNotFoundError\nav NO ModuleNotFounā€¦", + "codex.duration_ms": 3608, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "8d2267da3c758e00", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692559625, + "endTime": 1788692559625.8872, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_2", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'command -v swift || true\ncommand -v qlmanage || true\ncommand -v afconvert || true\ncommand -v mediainfo || true\ncommand -v mpv || true\ncommand -v vlc || true'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "/usr/bin/swift\n/usr/bin/qlmanage\n/usr/bin/afconvert\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "b175f400e4de38d3", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692600602, + "endTime": 1788692603579.6816, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_3", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p tmpframes\nTMPDIR=\"$PWD/.tmp\" CLANG_MODULE_CACHE_PATH=\"$PWD/.tmp/clang\" SWIFT_MODULECACHE_PATH=\"$PWD/.tmp/swift\" swift - media/ZYTmgi1pAIE.mp4 tmpframes <<'\"'SWIFT'\nimport Foundation\nimport AVFoundation\nimport AppKit\nlet args=CommandLine.arguments\nlet asset=AVAsset(url: URL(fileURLWithPath: args[1]))\nlet gen=AVAssetImageGenerator(asset: asset)\ngen.appliesPreferredTrackTransfoā€¦", + "promptfoo.span.role": "target", + "codex.exit_code": 1, + "codex.status": "failed", + "codex.output": "2026-09-06 16:03:20.620 xcodebuild[30704:9207466] DVTFilePathFSEvents: Failed to start fs event stream.\n2026-09-06 16:03:21.389 xcodebuild[30705:9207511] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-mSndOS6R' (errno=Operation not permitted)\n2026-09-06 16:03:22.097 xcodebuild[30707:9207521ā€¦", + "codex.duration_ms": 2978, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 1" + }, + { + "spanId": "a07f6febd9ca853a", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692616562, + "endTime": 1788692625863.8914, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_4", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p .tmp/clang .tmp/swift tmpframes\nTMPDIR=\"$PWD/.tmp\" CLANG_MODULE_CACHE_PATH=\"$PWD/.tmp/clang\" SWIFT_MODULECACHE_PATH=\"$PWD/.tmp/swift\" swift - media/ZYTmgi1pAIE.mp4 tmpframes <<'\"'SWIFT'\nimport Foundation\nimport AVFoundation\nimport AppKit\nlet args=CommandLine.arguments\nlet asset=AVAsset(url: URL(fileURLWithPath: args[1]))\nlet gen=AVAssetImageGenerator(asset: asset)\ngen.applieā€¦", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 16:03:36.582 xcodebuild[30727:9207659] DVTFilePathFSEvents: Failed to start fs event stream.\n2026-09-06 16:03:37.306 xcodebuild[30728:9207694] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-d7dnI3MX' (errno=Operation not permitted)\n2026-09-06 16:03:38.024 xcodebuild[30730:9207707ā€¦", + "codex.duration_ms": 9302, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "5f7bf04d9aa81038", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692645842, + "endTime": 1788692660604.592, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_5", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'curl -L --fail --silent --show-error -o .tmp/ffmpeg.zip https://evermeet.cx/ffmpeg/getrelease/zip && unzip -o .tmp/ffmpeg.zip -d .tmp/ffmpeg-bin && .tmp/ffmpeg-bin/ffmpeg -version | head -n 2'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Archive: .tmp/ffmpeg.zip\n inflating: .tmp/ffmpeg-bin/ffmpeg \nffmpeg version 9.0.1-tessus https://evermeet.cx/ffmpeg/ Copyright (c) 2000-2026 the FFmpeg developers\nbuilt with Apple clang version 17.0.0 (clang-1700.6.4.2)\n", + "codex.duration_ms": 14763, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "fbbaae7cf5562d28", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692673015, + "endTime": 1788692673720.6035, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_6", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc '.tmp/ffmpeg-bin/ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \"fps=1/3,scale=320:-1,tile=5x6\" -frames:v 1 tmpframes/contact.jpg && ls -lh tmpframes/contact.jpg'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "-rw-r--r--@ 1 staff 118K Sep 6 16:04 tmpframes/contact.jpg\n", + "codex.duration_ms": 706, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "b4bac974202752c3", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692702163, + "endTime": 1788692702264.5393, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_7", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc '.tmp/ffmpeg-bin/ffmpeg -hide_banner -loglevel error -ss 64 -i media/ZYTmgi1pAIE.mp4 -t 11.8 -vf \"fps=2,scale=320:-1,tile=6x4\" -frames:v 1 tmpframes/end_contact.jpg'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + "codex.duration_ms": 101, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "ec959cf0b70dd4f7", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692770302, + "endTime": 1788692770347.9265, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_8", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc '.tmp/ffmpeg-bin/ffmpeg -hide_banner -loglevel error -ss 65.8 -i media/ZYTmgi1pAIE.mp4 -t 10 -vn -ac 1 -ar 22050 -b:a 48k -f mp3 - | base64'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "l8+mmbiGdlLNXQAIiHlla/3ttxr6aDlE1z2yIWPPJW8yk/21rrOkQ4MCbGBwYYHSNIJ8L4jBLn2fpspZs1dBk1IuZHq1Im6aSabIux167rqPJDlNXWi/9UikgCxG0PAwhEm1H//WyzBA//NixPsm9BazCHobjLxMJUE9AmwlYwITVIegXgSY2OP////qdX/9E/nzQiiSDijMSxmz6aNTdCZpqQMRYhsJJGRORN1mC6oHiamnZX607PWpOfRMFKsZOtFJFBTKlwsOLU1a0DT/payRKkTNROGODmHoLUcw3kwpsj00oWujpm8owLBmoGaAY4iK8Biq5mIikaVo6d6sgtgsCEEhh//zYsTvJgwOpwABmlmBfEA8Zv//1V3dwChQXhTD4KYej4qcvdv/ā€¦", + "codex.duration_ms": 45, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "e7fdd77817dcac8e", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692813814, + "endTime": 1788692813922.158, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_9", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc '.tmp/ffmpeg-bin/ffmpeg -hide_banner -ss 64 -i media/ZYTmgi1pAIE.mp4 -t 11.8 -af silencedetect=noise=-35dB:d=0.15 -f null - 2>&1 | tail -n 30'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": " Stream #0:1 -> #0:1 (aac (native) -> pcm_s16le (native))\nPress [q] to stop, [?] for help\nOutput #0, null, to 'pipe:':\n Metadata:\n major_brand : mp42\n minor_version : 0\n compatible_brands: isommp42\n encoder : Lavf63.1.101\n Stream #0:0(und): Video: wrapped_avframe, yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], q=2-31, 200 kb/s, 29.97 fps, 29.97 tbn (defaā€¦", + "codex.duration_ms": 108, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "5a899b555495e433", + "parentSpanId": "53481caf1cb84ee0", + "name": "exec /bin/zsh", + "startTime": 1788692829072, + "endTime": 1788692829297.4258, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_10", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc '.tmp/ffmpeg-bin/ffmpeg -hide_banner -loglevel error -ss 68 -i media/ZYTmgi1pAIE.mp4 -t 7.8 -lavfi \"showspectrumpic=s=1200x500:legend=1:color=intensity:scale=log\" -frames:v 1 tmpframes/end_spectrum.png'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + "codex.duration_ms": 226, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "3bf8766b5411a876", + "parentSpanId": "53481caf1cb84ee0", + "name": "agent response", + "startTime": 1788692829298, + "endTime": 1788692847906, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_11", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The event occurs in the closing sequence near the end of the video.\",\"source_job_id\":null,\"candidates\":[{\"start_seconds\":65.8,\"end_seconds\":75.8,\"modalities\":[\"scene\",\"action\",\"sound\"],\"description\":\"Closing clip containing the transition to a starry night sky, the flashing SUBSCRIBE message, and the sustained echoing bell sound through the end.\",\"evidence_ids\":ā€¦", + "codex.duration_ms": 18607, + "codex.had_start_event": false + }, + "statusCode": 1 + }, + { + "spanId": "03c8949895fead15", + "parentSpanId": "53481caf1cb84ee0", + "name": "gen_ai.turn 1", + "startTime": 1788692463426, + "endTime": 1788692847923, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "gen_ai.turn.index": 1, + "gen_ai.provider.name": "openai", + "promptfoo.span.role": "target", + "gen_ai.usage.input_tokens": 404484, + "gen_ai.usage.output_tokens": 3772, + "gen_ai.usage.cache_read.input_tokens": 367616, + "gen_ai.usage.reasoning.output_tokens": 1415 + }, + "statusCode": 1 + }, + { + "spanId": "53481caf1cb84ee0", + "parentSpanId": "3b6efe323ff85f53", + "name": "invoke_agent Codex", + "startTime": 1788692462291, + "endTime": 1788692851006.2463, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "gen_ai.provider.name": "openai", + "gen_ai.operation.name": "invoke_agent", + "promptfoo.provider.id": "openai:codex-sdk", + "gen_ai.agent.name": "Codex", + "gen_ai.request.model": "gpt-5.6-sol", + "promptfoo.span.role": "target", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.request.body": "Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + }, + { + "traceId": "52632743c4b222bc72d2adc8b6f36e01", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "4-2", + "metadata": { + "testIdx": 4, + "promptIdx": 2, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZYT-bell-subscribe", + "dataset": "LongVALE evaluation", + "video_id": "ZYTmgi1pAIE", + "media_relpath": "media/ZYTmgi1pAIE.mp4", + "duration_seconds": 75.813152, + "event_index": 2, + "query": "a ringing bell echoes while SUBSCRIBE flashes over a starry night sky", + "expected_start": 70.07, + "expected_end": 75.742, + "modalities": "[\"scene\",\"sound\"]", + 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xcodebuild[31036:9210293] DVTFilePathFSEvents: Failed to start fs event stream.\ncv2 no\nimageio no\nmoviepy no\nav no\nlibrosa no\nā€¦", + "codex.duration_ms": 3153, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "d82d374c4286077b", + "parentSpanId": "a2e68149b1937989", + "name": "agent response", + "startTime": 1788692948101, + "endTime": 1788692953228, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_3", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The local system lacks a media decoder, so Iā€™m setting up a workspace-local decoder to inspect the supplied file directly.\",\"source_job_id\":null,\"candidates\":[]}", + "codex.duration_ms": 5127, + "codex.had_start_event": false + }, + "statusCode": 1 + }, + { + "spanId": "71150fd7e4fcf7ae", + 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.inspection/contact.jpg.\nError opening output files: No such file or directory\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 254" + }, + { + "spanId": "1395f95d227b8710", + "parentSpanId": "a2e68149b1937989", + "name": "exec /bin/zsh", + "startTime": 1788692990899, + "endTime": 1788692990899.6807, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_6", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \".tools/ffmpeg/ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \\\"fps=1/4,scale=320:-1,drawtext=fontfile=/System/Library/Fonts/Helvetica.ttc:text='%{pts\\\\\\\\:hms}':x=8:y=8:fontsize=20:fontcolor=white:borderw=2:bordercolor=black,tile=5x4:padding=4:margin=4\\\" -frames:v 1 .inspection/contact.jpg\"", + "promptfoo.span.role": "target", + "codex.exit_code": 254, + "codex.status": 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\"showspectrumpic=s=1200x500:legend=1:scale=log:color=channel\" .inspection/end_spectrum.png -y'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + "codex.duration_ms": 199, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "f54fd2a6b42bfc7b", + "parentSpanId": "a2e68149b1937989", + "name": "exec /bin/zsh", + "startTime": 1788693044269, + "endTime": 1788693044300.051, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_11", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc '.tools/ffmpeg/ffmpeg -hide_banner -loglevel error -ss 68 -i media/ZYTmgi1pAIE.mp4 -t 5 -vf \"fps=4,scale=320:-1,tile=5x4:padding=4:margin=4\" -frames:v 1 .inspection/transition_contact.jpg'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: ZYTmgi1pAIE\nLocal media path, when available: media/ZYTmgi1pAIE.mp4\nVideo duration: 75.813152 seconds\nEvent to locate: a ringing bell echoes while SUBSCRIBE flashes over a starry nightā€¦", + "gen_ai.usage.input_tokens": 318884, + "gen_ai.usage.output_tokens": 3140, + "promptfoo.usage.total_tokens": 322024, + "gen_ai.usage.cache_read.input_tokens": 284928, + "gen_ai.usage.reasoning.output_tokens": 1005, + "gen_ai.response.model": "gpt-5.6-sol", + "gen_ai.response.id": "01a07667-e9ba-7a73-8186-5ab1dd755bc5", + "promptfoo.response.body": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"One distinct matching event is grounded near the outro: the video transitions to a starry night graphic at about 69.75 seconds, ā€œSUBSCRIBEā€ flashes into view, and the audio changes to a sustained, echoing bell-like outro sound.\",\"source_job_id\":null,\"candidates\":[{\"start_seconds\":65.8,\"end_seconds\":75.8,\"modalities\":[\"scene\",\"sound\"],\"description\":\"Most likely cā€¦", + "codex.conversation.message_count": 4, + "codex.items.total": 13, + "codex.items.breakdown": "{\"agent_message\":3,\"command_execution\":10}" + }, + "statusCode": 1 + }, + { + "spanId": "d1aa9356d787a794", + "parentSpanId": "3b6f18a9c8f0920a", + "name": "codex-clean-user", + "startTime": 1788692916635, + "endTime": 1788693055707.8467, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.span.role": "target", + "promptfoo.target.type": "provider", + "promptfoo.target.label": "codex-clean-user", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: ZYTmgi1pAIE\nLocal media path, when available: media/ZYTmgi1pAIE.mp4\nVideo duration: 75.813152 seconds\nEvent to locate: a ringing bell echoes while SUBSCRIBE flashes over a starry nightā€¦", + "gen_ai.usage.input_tokens": 191737, + "gen_ai.usage.output_tokens": 1308, + "promptfoo.usage.total_tokens": 193045, + "gen_ai.usage.cache_read.input_tokens": 144896, + "gen_ai.usage.reasoning.output_tokens": 417, + "gen_ai.response.model": "gpt-5.6-sol", + "gen_ai.response.id": "01a0766a-0d3d-74d1-9350-e554172b4b2b", + "promptfoo.response.body": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The event is best supported in the closing sequence around 70.0ā€“75.8 seconds. The 10-second clip is shifted earlier to accommodate the end of the video.\",\"source_job_id\":\"bad0c141a34040809db9df12f3fcce10\",\"candidates\":[{\"start_seconds\":65.813,\"end_seconds\":75.813,\"modalities\":[\"scene\",\"action\",\"sound\"],\"description\":\"Closing starry-sky SUBSCRIBE animation with tā€¦", + "codex.conversation.message_count": 3, + "codex.items.total": 8, + "codex.items.breakdown": "{\"agent_message\":2,\"command_execution\":1,\"mcp_tool_call\":5}" + }, + "statusCode": 1 + }, + { + "spanId": "427eadd6646ac1e7", + "parentSpanId": "cad50918f768b661", + "name": "codex-vidxp", + "startTime": 1788693056133, + "endTime": 1788693126140.0256, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.span.role": "target", + "promptfoo.target.type": "provider", + "promptfoo.target.label": "codex-vidxp", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 5 + }, + "statusCode": 1 + }, + { + "spanId": "429f3da9ba15a077", + "parentSpanId": "cad50918f768b661", + "name": "grader is-json", + "startTime": 1788693126412, + "endTime": 1788693126413.1028, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 5, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "74d62718afdd30c2", + "parentSpanId": "cad50918f768b661", + "name": "grader python", + "startTime": 1788693126413, + "endTime": 1788693126515.1592, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 5, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Bounded chunk hit in 1 candidate(s); top-1 hit, first hit rank 1, best coverage 1.0000, best temporal IoU 0.5672." + }, + "statusCode": 1 + }, + { + "spanId": "4a9d972386754ec6", + "parentSpanId": "cad50918f768b661", + "name": "grader python", + "startTime": 1788693126413, + "endTime": 1788693127111.329, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 5, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "VidXP-on returned evidence from a fresh, successful, matching MCP job." + }, + "statusCode": 1 + }, + { + "spanId": "cad50918f768b661", + "name": "promptfoo.test_case", + "startTime": 1788693056132, + "endTime": 1788693127111.064, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 5, + "promptfoo.test_case.id": "5-0", + "promptfoo.prompt.index": 0, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + }, + { + "traceId": "135d33e6eb7548b7ba1f0c8429cad8f1", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "6-2", + "metadata": { + "testIdx": 6, + "promptIdx": 2, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZYT-bell-subscribe", + "dataset": "LongVALE evaluation", + "video_id": "ZYTmgi1pAIE", + "media_relpath": "media/ZYTmgi1pAIE.mp4", + "duration_seconds": 75.813152, + "event_index": 2, + "query": "a ringing bell echoes while SUBSCRIBE flashes over a starry night sky", + "expected_start": 70.07, + "expected_end": 75.742, + "modalities": "[\"scene\",\"sound\"]", + "condition": "clean-user", + "expected_vidxp": false, + "allow_media_shell": true, + "forbid_host_tools": true, + "evaluation_mode": "pilot", + "repetition": 3, + "target_chunk_seconds": 10, + "min_chunk_seconds": 8, + "max_chunk_seconds": 12, + "min_event_coverage": 0.5, + "max_candidates": 3 + } + }, + "spans": [ + { + "spanId": "750c81924c75233c", + "parentSpanId": "a2432d9264cdcc57", + "name": "agent response", + "startTime": 1788693127338, + "endTime": 1788693135700, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_0", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"Iā€™m inspecting the supplied videoā€™s visuals and audio to ground the clip timing.\",\"source_job_id\":null,\"candidates\":[]}", + "codex.duration_ms": 8362, + "codex.had_start_event": false + }, + "statusCode": 1 + }, + { + "spanId": "f5a57999d21b42d7", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693140087, + "endTime": 1788693140087.641, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'ffprobe -v error -show_entries format=duration -show_streams -of json media/ZYTmgi1pAIE.mp4 && mkdir -p /tmp/vidframes_ZYTmgi1pAIE && ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \"fps=1/3,scale=320:-1,tile=5x6\" -frames:v 1 /tmp/vidframes_ZYTmgi1pAIE/contact.jpg'", + "promptfoo.span.role": "target", + "codex.exit_code": 127, + "codex.status": "failed", + "codex.output": "zsh:1: command not found: ffprobe\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 127" + }, + { + "spanId": "700eaaaf2c40711c", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693163934, + "endTime": 1788693167445.605, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_2", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'for x in python3 mediainfo mpv vlc exiftool; do command -v \"$x\" || true; done\npython3 - <<'\"'PY'\nmods=['cv2','imageio','moviepy','av','numpy','PIL','librosa','soundfile']\nfor m in mods:\n try:\n __import__(m); print(m,'ok')\n except Exception as e: print(m,'no')\nPY\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 16:12:44.497 xcodebuild[31379:9213707] DVTFilePathFSEvents: Failed to start fs event stream.\npython3: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-Ujq4TvKb' (errno=Operation not permitted)\n2026-09-06 16:12:46.117 xcodebuild[31383:9213740] DVTFilePathFSEvents: Failed to start fs event stream.\ncv2 no\nimageio no\nmoviepy no\nav no\nnumpy no\nPIā€¦", + "codex.duration_ms": 3511, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "4a73562c81785bce", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693173595, + "endTime": 1788693175692.8982, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_3", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'ls -lh media/ZYTmgi1pAIE.mp4\nfile media/ZYTmgi1pAIE.mp4\nstrings -a media/ZYTmgi1pAIE.mp4 | rg -i -n \"subscribe|star|bell\" | head -30'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 16:12:53.611 xcodebuild[31395:9213840] DVTFilePathFSEvents: Failed to start fs event stream.\nstrings: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-dYa8fOlF' (errno=Operation not permitted)\n2026-09-06 16:12:54.325 xcodebuild[31399:9213865] DVTFilePathFSEvents: Failed to start fs event stream.\n", + "codex.duration_ms": 2098, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "97d99eb93d914456", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693180665, + "endTime": 1788693180666.365, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_4", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'export TMPDIR=\"$PWD/.tmp\"; mkdir -p \"$TMPDIR\"; command -v swift || true; command -v qlmanage || true; command -v sips || true; command -v mdls || true'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "/usr/bin/swift\n/usr/bin/qlmanage\n/usr/bin/sips\n/usr/bin/mdls\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "c4c4eaddee37642e", + "parentSpanId": "a2432d9264cdcc57", + "name": "file add", + "startTime": 1788693191120, + "endTime": 1788693191186.364, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_5", + "codex.item.type": "file_change", + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.status": "completed", + "codex.files_changed": 1, + "codex.files": "/.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/extract_frames.swift", + "codex.duration_ms": 67, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "10a8487432a1fb98", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693198608, + "endTime": 1788693208656.5242, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_6", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'export TMPDIR=\"$PWD/.tmp\"; mkdir -p \"$TMPDIR\" .swift-cache frames3; swift -module-cache-path \"$PWD/.swift-cache\" extract_frames.swift \"$PWD/media/ZYTmgi1pAIE.mp4\" \"$PWD/frames3\" 3'", + "promptfoo.span.role": "target", + "codex.exit_code": 133, + "codex.status": "failed", + "codex.output": "2026-09-06 16:13:18.911 xcodebuild[31457:9214498] DVTFilePathFSEvents: Failed to start fs event stream.\n2026-09-06 16:13:20.222 xcodebuild[31458:9214530] DVTFilePathFSEvents: Failed to start fs event stream.\nswift: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-sq0NLS8t' (errno=Operation not permitted)\n2026-09-06 16:13:20.944 xcodebuild[31460:9214547ā€¦", + "codex.duration_ms": 10047, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 133" + }, + { + "spanId": "d9fbdff7bada82a3", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693215689, + "endTime": 1788693230590.1392, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_7", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'curl -L --fail --silent --show-error https://evermeet.cx/ffmpeg/getrelease/zip -o ffmpeg.zip && unzip -o ffmpeg.zip && chmod +x ffmpeg && ./ffmpeg -version | head -2'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Archive: ffmpeg.zip\n inflating: ffmpeg \nffmpeg version 9.0.1-tessus https://evermeet.cx/ffmpeg/ Copyright (c) 2000-2026 the FFmpeg developers\nbuilt with Apple clang version 17.0.0 (clang-1700.6.4.2)\n", + "codex.duration_ms": 14901, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "429e2843bc79f088", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693236933, + "endTime": 1788693236933.74, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_8", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"mkdir -p frames2\n./ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \\\"fps=1/2,scale=320:-2,drawtext=text='%{pts\\\\\\\\:hms}':x=8:y=8:fontsize=20:fontcolor=white:box=1:boxcolor=black@0.6,tile=5x4\\\" -vsync vfr frames2/contact_%02d.jpg\nls frames2\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Unrecognized option 'vsync'.\nError splitting the argument list: Option not found\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "bd039e87ba32a576", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693245137, + "endTime": 1788693245137.6477, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_9", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"./ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \\\"fps=1/2,scale=320:-2,drawtext=text='%{pts\\\\\\\\:hms}':x=8:y=8:fontsize=20:fontcolor=white:box=1:boxcolor=black@0.6,tile=5x4\\\" -fps_mode vfr frames2/contact_%02d.jpg\nls frames2\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Fontconfig error: Cannot load default config file: File not found\nFontconfig error: Cannot load default config file: File not found\n[Parsed_drawtext_2 @ 0x7fae67007240] Cannot find a valid font for the family Sans\n[AVFilterGraph @ 0x7fae67006880] Error initializing filters\nError opening output file frames2/contact_%02d.jpg.\nError opening output files: No such file or directory\n", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "e77bcfc0edabcb61", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693252675, + "endTime": 1788693252675.6213, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_10", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"./ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \\\"fps=1/2,scale=320:-2,drawtext=fontfile=/System/Library/Fonts/Helvetica.ttc:text='%{pts\\\\\\\\:hms}':x=8:y=8:fontsize=20:fontcolor=white:box=1:boxcolor=black@0.6,tile=5x4\\\" -fps_mode vfr frames2/contact_%02d.jpg\nls frames2\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "Fontconfig error: Cannot load default config file: File not found\nFontconfig error: Cannot load default config file: File not found\n[Parsed_drawtext_2 @ 0x7fc8e9f16700] Cannot find a valid font for the family Sans\n[AVFilterGraph @ 0x7fc8e9f15dc0] Error initializing filters\nError opening output file frames2/contact_%02d.jpg.\nError opening output files: No such file or directory\n", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "643efcdd34ef057a", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693259343, + "endTime": 1788693260055.3557, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_11", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc './ffmpeg -hide_banner -loglevel error -i media/ZYTmgi1pAIE.mp4 -vf \"fps=1/2,scale=320:-2,tile=5x4\" -fps_mode vfr frames2/contact_%02d.jpg\nls frames2'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "contact_01.jpg\ncontact_02.jpg\n", + "codex.duration_ms": 712, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "241bcae9d1ce6b82", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693281981, + "endTime": 1788693283775.725, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_12", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p tail\n./ffmpeg -hide_banner -loglevel error -ss 64 -i media/ZYTmgi1pAIE.mp4 -t 11.813152 -vf \"fps=2,scale=320:-2,tile=6x4\" -frames:v 1 tail/contact.jpg\n./ffmpeg -hide_banner -loglevel error -ss 64 -i media/ZYTmgi1pAIE.mp4 -t 11.813152 -lavfi \"showspectrumpic=s=1200x500:legend=1:scale=log\" tail/spectrum.png\n./ffmpeg -hide_banner -i media/ZYTmgi1pAIE.mp4 -ss 64 -t 11.813152 -afā€¦", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "[Parsed_astats_0 @ 0x7fea0af70b40] Noise floor dB: -inf\n[Parsed_astats_0 @ 0x7fea0af70b40] Noise floor count: 13156\n[Parsed_astats_0 @ 0x7fea0af70b40] Entropy: 0.758594\n[Parsed_astats_0 @ 0x7fea0af70b40] Bit depth: 31/32/32/32\n[Parsed_astats_0 @ 0x7fea0af70b40] Dynamic range: 221.470316\n[Parsed_astats_0 @ 0x7fea0af70b40] Zero crossings: 375069\n[Parsed_astats_0 @ 0x7fea0af70b40] Zero crossings rateā€¦", + "codex.duration_ms": 1795, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "9c278cd38c84eb82", + "parentSpanId": "a2432d9264cdcc57", + "name": "exec /bin/zsh", + "startTime": 1788693301641, + "endTime": 1788693301695.8823, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_13", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc './ffmpeg -hide_banner -loglevel error -ss 69.5 -i media/ZYTmgi1pAIE.mp4 -t 6.3 -vn -ac 1 -c:a libopus -b:a 16k tail/event.ogg && base64 < tail/event.ogg'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "T2dnUwACAAAAAAAAAACkQCumAAAAALC8L2wBE09wdXNIZWFkAQE4AYC7AAAAAABPZ2dTAAAAAAAAAAAAAKRAK6YBAAAAnUiWvAL/Dk9wdXNUYWdzDAAAAExhdmY2My4xLjEwMQcAAAAcAAAAZW5jb2Rlcj1MYXZjNjMuMS4xMDEgbGlib3B1cykAAABjcmVhdGlvbl90aW1lPTIwMjQtMDMtMTZUMDA6MDg6NTguMDAwMDAwWgwAAABsYW5ndWFnZT1lbmdLAAAAaGFuZGxlcl9uYW1lPUlTTyBNZWRpYSBmaWxlIHByb2R1Y2VkIGJ5IEdvb2dsZSBJbmMuIENyZWF0ZWQgb246IDAzLzE1LzIwMjQuEAAAAG1ham9yX2JyYW5kPW1wNDIPAAAAā€¦", + "codex.duration_ms": 54, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "ff4c47df60ab3e26", + "parentSpanId": "a2432d9264cdcc57", + "name": "file delete", + "startTime": 1788693310418, + "endTime": 1788693310484.671, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_14", + "codex.item.type": "file_change", + "gen_ai.turn.index": 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 7 + }, + "statusCode": 1 + }, + { + "spanId": "cb985f996d0d8b25", + "parentSpanId": "f48c7f5b7e03013f", + "name": "grader is-json", + "startTime": 1788693403157, + "endTime": 1788693403158.02, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 7, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "788947bceaae21fe", + "parentSpanId": "f48c7f5b7e03013f", + "name": "grader python", + "startTime": 1788693403157, + "endTime": 1788693403255.344, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 7, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Bounded chunk hit in 1 candidate(s); top-1 hit, first hit rank 1, best coverage 1.0000, best temporal IoU 0.5672." + }, + "statusCode": 1 + }, + { + "spanId": "a712d66ab5968df0", + "parentSpanId": "f48c7f5b7e03013f", + "name": "grader python", + "startTime": 1788693403157, + "endTime": 1788693403829.5208, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 7, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "VidXP-on returned evidence from a fresh, successful, matching MCP job." + }, + "statusCode": 1 + }, + { + "spanId": "f48c7f5b7e03013f", + "name": "promptfoo.test_case", + "startTime": 1788693336648, + "endTime": 1788693403829.0784, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 7, + "promptfoo.test_case.id": "7-0", + "promptfoo.prompt.index": 0, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + }, + { + "traceId": "f3d07b28598847a7cfc522cb2fa9c373", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "8-1", + "metadata": { + "testIdx": 8, + "promptIdx": 1, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZYT-bell-subscribe", + "dataset": "LongVALE evaluation", + "video_id": "ZYTmgi1pAIE", + "media_relpath": "media/ZYTmgi1pAIE.mp4", + "duration_seconds": 75.813152, + "event_index": 2, + "query": "a ringing bell echoes while SUBSCRIBE flashes over a starry night sky", + "expected_start": 70.07, + "expected_end": 75.742, + "modalities": "[\"scene\",\"sound\"]", + 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.request.body": "Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: ZYTmgi1pAIE\nLocal media path, when available: media/ZYTmgi1pAIE.mp4\nVideo duration: 75.813152 seconds\nEvent to locate: a ringing bell echoes while SUBSCRIBE flashes over a starry nightā€¦", + "gen_ai.usage.input_tokens": 250325, + "gen_ai.usage.output_tokens": 1819, + "promptfoo.usage.total_tokens": 252144, + "gen_ai.usage.cache_read.input_tokens": 215168, + "gen_ai.usage.reasoning.output_tokens": 774, + "gen_ai.response.model": "gpt-5.6-sol", + "gen_ai.response.id": "01a0766f-5c70-7363-80ef-f1de1c9bbf3c", + "promptfoo.response.body": "{\"video_id\":\"ZYTmgi1pAIE\",\"answer\":\"The event occurs in the closing outro; one candidate is supported.\",\"source_job_id\":null,\"candidates\":[{\"start_seconds\":65.8,\"end_seconds\":75.8,\"modalities\":[\"scene\",\"sound\"],\"description\":\"Closing segment containing the transition to a starry night sky, flashing SUBSCRIBE text, and the accompanying resonant bell-like outro sound.\",\"evidence_ids\":[]}]}", + "codex.conversation.message_count": 3, + "codex.items.total": 6, + "codex.items.breakdown": "{\"agent_message\":2,\"command_execution\":4}" + }, + "statusCode": 1 + }, + { + "spanId": "7d20d7ae91988ed2", + "parentSpanId": "1e341fb6798165cd", + "name": "codex-baseline", + "startTime": 1788693403865, + "endTime": 1788693479891.1833, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.span.role": "target", + "promptfoo.target.type": "provider", + "promptfoo.target.label": "codex-baseline", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 8 + }, + "statusCode": 1 + }, + { + "spanId": "a6bce1ab7c790f4d", + "parentSpanId": "1e341fb6798165cd", + "name": "grader is-json", + "startTime": 1788693480164, + "endTime": 1788693480164.7207, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 8, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "3733e5dbc9cddf05", + "parentSpanId": "1e341fb6798165cd", + "name": "grader python", + "startTime": 1788693480165, + "endTime": 1788693480261.038, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 8, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Bounded chunk hit in 1 candidate(s); top-1 hit, first hit rank 1, best coverage 1.0000, best temporal IoU 0.5672." + }, + "statusCode": 1 + }, + { + "spanId": "8c97ece464ea0e43", + "parentSpanId": "1e341fb6798165cd", + "name": "grader python", + "startTime": 1788693480165, + "endTime": 1788693480261.6711, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 8, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "The condition remained isolated from VidXP and respected its tool policy." + }, + "statusCode": 1 + }, + { + "spanId": "1e341fb6798165cd", + "name": "promptfoo.test_case", + "startTime": 1788693403863, + "endTime": 1788693480260.4807, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 8, + "promptfoo.test_case.id": "8-1", + "promptfoo.prompt.index": 1, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": true, + "promptfoo.test.score": 1 + }, + "statusCode": 1 + } + ] + }, + { + "traceId": "c9440aaf21f9a958d94a0c6355db715a", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "9-0", + "metadata": { + "testIdx": 9, + "promptIdx": 0, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZId-car-siren", + "dataset": "LongVALE evaluation", + "video_id": "ZIdFAGJrlCw", + "media_relpath": "media/ZIdFAGJrlCw.mp4", + "duration_seconds": 296.402721, + "event_index": 0, + "query": "a red car speeds down a winding road as a siren suddenly blares", + "expected_start": 7.68, + "expected_end": 10.2, + "modalities": "[\"action\",\"sound\"]", + "condition": "vidxp-on", + "expected_vidxp": true, + "allow_media_shell": false, + "forbid_host_tools": false, + "evaluation_mode": "pilot", + "repetition": 1, + "target_chunk_seconds": 10, + "min_chunk_seconds": 8, + "max_chunk_seconds": 12, + "min_event_coverage": 0.5, + "max_candidates": 3 + } + }, + "spans": [ + { + "spanId": "ecd412d52279899d", + "parentSpanId": "ad60a446adf4efc4", + "name": "agent response", + "startTime": 1788693480442, + "endTime": 1788693487938, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_0", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZIdFAGJrlCw\",\"answer\":\"Iā€™m using the video-evidence skill to locate and ground the event in indexed visual and audio evidence.\",\"source_job_id\":null,\"candidates\":[]}", + "codex.duration_ms": 7495, + "codex.had_start_event": false + }, + "statusCode": 1 + }, + { + "spanId": "6e894140d313473f", + "parentSpanId": "ad60a446adf4efc4", + "name": "exec /bin/zsh", + "startTime": 1788693490020, + "endTime": 1788693490020.9197, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"sed -n '1,240p' /.local/share/vidxp/benchmarks/codex-mcp/workspace/vidxp-on/.agents/skills/vidxp-find-video-evidence/SKILL.md\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "---\nname: vidxp-find-video-evidence\ndescription: Use VidXP to search indexed videos and surface inspectable evidence boards, keyframes, and clips before analysis. Trigger for requests such as \"find where X appears,\" \"when does Y happen,\" \"what is said,\" \"what happens,\" or \"show me the matching clip,\" even when the user does not name VidXP. 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 10 + }, + "statusCode": 1 + }, + { + "spanId": "0dc78ee37234d458", + "parentSpanId": "5dea90dd813db637", + "name": "grader is-json", + "startTime": 1788693906301, + "endTime": 1788693906302.9443, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "is-json", + "gen_ai.evaluation.name": "is-json", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 10, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "Assertion passed" + }, + "statusCode": 1 + }, + { + "spanId": "58a7864e25d9721e", + "parentSpanId": "5dea90dd813db637", + "name": "grader python", + "startTime": 1788693906301, + "endTime": 1788693906417.4685, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 10, + "gen_ai.evaluation.score.label": "fail", + "gen_ai.evaluation.score.value": 0, + "gen_ai.evaluation.explanation": "Bounded chunk miss in 1 candidate(s); top-1 miss, first hit rank none, best coverage 0.0000, best temporal IoU 0.0000." + }, + "statusCode": 1 + }, + { + "spanId": "c9a0cb69bebcbca5", + "parentSpanId": "5dea90dd813db637", + "name": "grader python", + "startTime": 1788693906302, + "endTime": 1788693906422.3384, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.grader.id": "python", + "gen_ai.evaluation.name": "python", + "promptfoo.span.role": "grader", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 10, + "gen_ai.evaluation.score.label": "pass", + "gen_ai.evaluation.score.value": 1, + "gen_ai.evaluation.explanation": "The condition remained isolated from VidXP and respected its tool policy." + }, + "statusCode": 1 + }, + { + "spanId": "5dea90dd813db637", + "name": "promptfoo.test_case", + "startTime": 1788693596037, + "endTime": 1788693906438.3667, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "promptfoo.span.role": "test_case", + "promptfoo.eval.id": "eval-7VR-2026-09-06T10:58:07", + "promptfoo.test.index": 10, + "promptfoo.test_case.id": "10-1", + "promptfoo.prompt.index": 1, + "promptfoo.provider.id": "openai:codex-sdk", + "promptfoo.prompt.label": "Fixed video evidence task: prompts/video-evidence.txt: Locate one event in the supplied video and return up to three practical candidate\nclips, ordered from most to least likely. Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": false, + "promptfoo.test.score": 0.6666666666666666 + }, + "statusCode": 2, + "statusMessage": "Bounded chunk miss in 1 candidate(s); top-1 miss, first hit rank none, best coverage 0.0000, best temporal IoU 0.0000." + } + ] + }, + { + "traceId": "4e3aad3f55f1c416fb35b1c123fe331a", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "11-2", + "metadata": { + "testIdx": 11, + "promptIdx": 2, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZId-car-siren", + "dataset": "LongVALE evaluation", + "video_id": "ZIdFAGJrlCw", + "media_relpath": "media/ZIdFAGJrlCw.mp4", + "duration_seconds": 296.402721, + "event_index": 0, + "query": "a red car speeds down a winding road as a siren suddenly blares", + "expected_start": 7.68, + "expected_end": 10.2, + "modalities": "[\"action\",\"sound\"]", + "condition": "clean-user", + "expected_vidxp": false, + "allow_media_shell": true, + "forbid_host_tools": true, + "evaluation_mode": "pilot", + "repetition": 1, + "target_chunk_seconds": 10, + "min_chunk_seconds": 8, + "max_chunk_seconds": 12, + "min_event_coverage": 0.5, + "max_candidates": 3 + } + }, + "spans": [ + { + "spanId": "dae8565e29e39840", + "parentSpanId": "6e16161f14541519", + "name": "agent response", + "startTime": 1788693907713, + "endTime": 1788693914201, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_0", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZIdFAGJrlCw\",\"answer\":\"Iā€™m inspecting the videoā€™s visual and audio timeline to ground the event and select the strongest 8ā€“12 second window.\",\"source_job_id\":null,\"candidates\":[]}", + "codex.duration_ms": 6488, + "codex.had_start_event": false + }, + "statusCode": 1 + }, + { + "spanId": "f4ddcc0f99f53e89", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693921111, + "endTime": 1788693921111.6885, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'ffprobe -v error -show_entries format=duration -show_streams -of json media/ZIdFAGJrlCw.mp4 && mkdir -p inspect && ffmpeg -hide_banner -loglevel error -i media/ZIdFAGJrlCw.mp4 -vf \"fps=1/5,scale=320:-1,tile=5x4\" -q:v 3 inspect/contact_%02d.jpg && ls -lh inspect'", + "promptfoo.span.role": "target", + "codex.exit_code": 127, + "codex.status": "failed", + "codex.output": "zsh:1: command not found: ffprobe\n", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 127" + }, + { + "spanId": "e6399116e3cce338", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693927577, + "endTime": 1788693930817.8225, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_2", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"command -v python3; command -v mediainfo; command -v mpv; command -v vlc; python3 - <<'PY'\nmods=['cv2','moviepy','av','imageio','librosa','soundfile','torch','transformers']\nfor m in mods:\n try:\n x=__import__(m); print(m,'ok')\n except Exception as e: print(m,'no',type(e).__name__)\nPY\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "2026-09-06 16:25:27.860 xcodebuild[32466:9224171] DVTFilePathFSEvents: Failed to start fs event stream.\npython3: error: couldn't create cache file '/var/folders/gm/vk4b_30d2hbdrl8g3z61rsyc0000gp/T/xcrun_db-VdOVRzM2' (errno=Operation not permitted)\n2026-09-06 16:25:29.476 xcodebuild[32468:9224203] DVTFilePathFSEvents: Failed to start fs event stream.\ncv2 no ModuleNotFoundError\nmoviepy no ModuleNoā€¦", + "codex.duration_ms": 3240, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "e05b31cba4b5474e", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693942496, + "endTime": 1788693942496.702, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_3", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'for x in avconvert qlmanage sips afinfo afconvert mdls; do command -v $x || true; done; ls -lh media/ZIdFAGJrlCw.mp4'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "/usr/bin/avconvert\n/usr/bin/qlmanage\n/usr/bin/sips\n/usr/bin/afinfo\n/usr/bin/afconvert\n/usr/bin/mdls\n-rw-r--r--@ 5 staff 54M Sep 6 15:40 media/ZIdFAGJrlCw.mp4\n", + "codex.duration_ms": 0, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "8abe5f9c1021a67c", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693949853, + "endTime": 1788693950398.4294, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_4", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p inspect/clips inspect/thumbs; avconvert --source media/ZIdFAGJrlCw.mp4 --output inspect/clips/test.mov --preset PresetPassthrough --start 120 --duration 10 --replace --progress; qlmanage -t -s 480 -o inspect/thumbs inspect/clips/test.mov >/dev/null 2>&1; find inspect -maxdepth 2 -type f -print'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "avconvert: specified file extension:mov not valid with preset:PresetPassthrough\n Valid file extensions:\n", + "codex.duration_ms": 546, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "ab350863b498281f", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693954887, + "endTime": 1788693954887.8027, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_5", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'avconvert --help | head -80'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "usage: avconvert [--param ...]\n\n Required parameters:\n --preset (or -p) Use --help to list all available preset names.\n --source (or -s) \n --output (or -o) \n\n Optional parameters:\n --disableFastStart Disable fast-start movie creation. Reduces disk accesses if fast-start ā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "166d6c13b17b7c27", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693961700, + "endTime": 1788693961700.6787, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_6", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'avconvert -s media/ZIdFAGJrlCw.mp4 -p PresetLowQuality -o inspect/clips/test.m4v --start 120 --duration 10 --replace --progress; qlmanage -t -s 480 -o inspect/thumbs inspect/clips/test.m4v >/dev/null 2>&1; find inspect -maxdepth 2 -type f -print'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "sysctlbyname for kern.hv_vmm_present failed with status -1avconvert: invalid configuration (preset name PresetLowQuality) with media/ZIdFAGJrlCw.mp4 -- file:///.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/\n Presets compatible with media/ZIdFAGJrlCw.mp4 -- file:///.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/:\n Preset640x480\n ā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "b08aa22fa44bb54b", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693969040, + "endTime": 1788693969040.7043, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_7", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'avconvert -s /.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/media/ZIdFAGJrlCw.mp4 -p Preset640x480 -o /.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/inspect/clips/test.m4v --start 120 --duration 10 --replace --progress'", + "promptfoo.span.role": "target", + "codex.exit_code": 205, + "codex.status": "failed", + "codex.output": "sysctlbyname for kern.hv_vmm_present failed with status -1avconvert: invalid configuration (preset name Preset640x480) with file:///.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/media/ZIdFAGJrlCw.mp4\n Presets compatible with file:///.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/media/ZIdFAGJrlCw.mp4:\n Preset640x480\n Presetā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 205" + }, + { + "spanId": "79106adbb8e568cb", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693975602, + "endTime": 1788693975602.6619, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_8", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'avconvert -s /.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/media/ZIdFAGJrlCw.mp4 -p Preset640x480 -o /.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/inspect/clips/test.mov --start 120 --duration 10 --replace --progress'", + "promptfoo.span.role": "target", + "codex.exit_code": 205, + "codex.status": "failed", + "codex.output": "sysctlbyname for kern.hv_vmm_present failed with status -1avconvert: invalid configuration (preset name Preset640x480) with file:///.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/media/ZIdFAGJrlCw.mp4\n Presets compatible with file:///.local/share/vidxp/benchmarks/codex-mcp/workspace/clean-user/media/ZIdFAGJrlCw.mp4:\n Preset640x480\n Presetā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 205" + }, + { + "spanId": "aaa7d0d6ac6ba57c", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693981897, + "endTime": 1788693981897.6172, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_9", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'qlmanage -t -s 960 -o inspect/thumbs media/ZIdFAGJrlCw.mp4 >/dev/null 2>&1; find inspect/thumbs -type f -maxdepth 1 -print'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "e47c1dc509a10283", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693987387, + "endTime": 1788693987387.9026, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_10", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'npm view @ffmpeg-installer/darwin-arm64 version'", + "promptfoo.span.role": "target", + "codex.exit_code": 127, + "codex.status": "failed", + "codex.output": "zsh:1: command not found: npm\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 127" + }, + { + "spanId": "dfb09664abedbafb", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788693997284, + "endTime": 1788693997847.13, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_11", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'mkdir -p inspect/bin; curl -L --fail --silent --show-error https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffmpeg-6.1-osx-64.zip -o inspect/ffmpeg.zip; unzip -o inspect/ffmpeg.zip -d inspect/bin; file inspect/bin/*; chmod +x inspect/bin/*; inspect/bin/ffmpeg -version | head -1'", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "curl: (56) The requested URL returned error: 404\nunzip: cannot find or open inspect/ffmpeg.zip, inspect/ffmpeg.zip.zip or inspect/ffmpeg.zip.ZIP.\nzsh:1: no matches found: inspect/bin/*\nzsh:1: no matches found: inspect/bin/*\nzsh:1: no such file or directory: inspect/bin/ffmpeg\n", + "codex.duration_ms": 563, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "8a8a7dfdf40393aa", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788694003121, + "endTime": 1788694003749.663, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_12", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"curl -L --fail --silent --show-error https://api.github.com/repos/ffbinaries/ffbinaries-prebuilt/releases/latest | rg 'browser_download_url.*osx' | head\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "curl: (56) Failure writing output to destination, passed 1370 returned 0\n", + "codex.duration_ms": 628, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "af32c7a3fdaf9d4d", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788694009262, + "endTime": 1788694009623.7964, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_13", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"curl -L --fail --silent --show-error https://api.github.com/repos/ffbinaries/ffbinaries-prebuilt/releases/latest -o inspect/release.json; grep -o 'https:[\"'^\"]*osx[''^\"]*'\"' inspect/release.json | head\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": "", + "codex.duration_ms": 362, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "87be55aa6b4a4e22", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788694014206, + "endTime": 1788694014206.9849, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_14", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc \"grep -E 'tag_name|browser_download_url' inspect/release.json | head -30\"", + "promptfoo.span.role": "target", + "codex.exit_code": 0, + "codex.status": "completed", + "codex.output": " \"tag_name\": \"v6.1\",\n \"browser_download_url\": \"https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffmpeg-6.1-linux-32.zip\"\n \"browser_download_url\": \"https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffmpeg-6.1-linux-64.zip\"\n \"browser_download_url\": \"https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffmpeg-6.1-linuxā€¦", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "14fd03f038b5048e", + "parentSpanId": "6e16161f14541519", + "name": "exec /bin/zsh", + "startTime": 1788694022032, + "endTime": 1788694040207.1055, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_15", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'curl -L --fail --silent --show-error https://github.com/ffbinaries/ffbinaries-prebuilt/releases/download/v6.1/ffmpeg-6.1-macos-64.zip -o inspect/ffmpeg.zip; 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lavfi.scd.score=0.000\n[Parsed_metadata_1 @ 0x7fa9f8062880] lavfi.scd.score=1.173\n[Parsed_metadata_1 @ 0x7fa9f8062880] lavfi.scd.score=0.046\n[Parsed_metadata_1 @ 0x7fa9f8062880] lavfi.scd.score=0.015\n[Parsed_metadata_1 @ 0x7fa9f8062880] lavfi.scd.score=0.085\n[Parsed_metadata_1 @ 0x7fa9f8062880] lavfi.scd.score=0.030\n[Parsed_metadata_1 @ 0x7fa9f8062880] lavfi.scdā€¦", + "codex.duration_ms": 183, + "codex.had_start_event": true + }, + "statusCode": 1 + }, + { + "spanId": "c51b5a245da563fa", + "parentSpanId": "9837fbcf4cc202e1", + "name": "agent response", + "startTime": 1788694455169, + "endTime": 1788694460366, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_14", + "codex.item.type": "agent_message", + "codex.timing.estimated": true, + "gen_ai.turn.index": 1, + "promptfoo.span.role": "target", + "codex.message": "{\"video_id\":\"ZIdFAGJrlCw\",\"answer\":\"The strongest grounded match is the transition into the scenic driving 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Return fewer when the available evidence\ndoes not support distinct alternatives.\n\nVideo ID: {{ video_id }}\nLocal media path, when available: {{ media_relpath }}\nVideo duration: {{ duration_seconds }} seconds\nEvent to locaā€¦", + "promptfoo.repeat.index": 0, + "promptfoo.test.success": false, + "promptfoo.test.score": 0.6666666666666666 + }, + "statusCode": 2, + "statusMessage": "Bounded chunk miss in 1 candidate(s); top-1 miss, first hit rank none, best coverage 0.0000, best temporal IoU 0.0000." + } + ] + }, + { + "traceId": "6bff956f23b99dd9793a5862e26b7971", + "evaluationId": "eval-7VR-2026-09-06T10:58:07", + "testCaseId": "15-2", + "metadata": { + "testIdx": 15, + "promptIdx": 2, + "providerId": "openai:codex-sdk", + "repeatIndex": 0, + "vars": { + "id": "longvale-part9-ZId-car-siren", + "dataset": "LongVALE evaluation", + "video_id": "ZIdFAGJrlCw", + "media_relpath": "media/ZIdFAGJrlCw.mp4", + "duration_seconds": 296.402721, + "event_index": 0, + "query": "a red car speeds down a winding road as a siren suddenly blares", + "expected_start": 7.68, + "expected_end": 10.2, + "modalities": "[\"action\",\"sound\"]", + "condition": "clean-user", + "expected_vidxp": false, + "allow_media_shell": true, + "forbid_host_tools": true, + "evaluation_mode": "pilot", + "repetition": 3, + "target_chunk_seconds": 10, + "min_chunk_seconds": 8, + "max_chunk_seconds": 12, + "min_event_coverage": 0.5, + "max_candidates": 3 + } + }, + "spans": [ + { + "spanId": "fcb8935293e8294b", + "parentSpanId": "729b6761ad9e65db", + "name": "exec /bin/zsh", + "startTime": 1788694569730, + "endTime": 1788694569730.805, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_0", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'ls -lh media/ZIdFAGJrlCw.mp4 && command -v ffmpeg && command -v ffprobe'", + "promptfoo.span.role": "target", + "codex.exit_code": 1, + "codex.status": "failed", + "codex.output": "-rw-r--r--@ 5 staff 54M Sep 6 15:40 media/ZIdFAGJrlCw.mp4\n", + "codex.duration_ms": 1, + "codex.had_start_event": true + }, + "statusCode": 2, + "statusMessage": "Command exited with code 1" + }, + { + "spanId": "91c74ffdc7dcdd8b", + "parentSpanId": "729b6761ad9e65db", + "name": "exec /bin/zsh", + "startTime": 1788694574986, + "endTime": 1788694574986.7046, + "attributes": { + "service.name": "promptfoo", + "service.version": "0.122.2", + "codex.item.id": "item_1", + "codex.item.type": "command_execution", + "gen_ai.turn.index": 1, + "codex.command": "/bin/zsh -lc 'for x in ffmpeg ffprobe python3; 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