diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml index 13163e6c..cb7bc6c3 100644 --- a/.github/workflows/pages.yml +++ b/.github/workflows/pages.yml @@ -4,9 +4,6 @@ on: push: branches: - main - paths: - - site/** - - .github/workflows/pages.yml workflow_dispatch: permissions: @@ -28,15 +25,29 @@ jobs: - name: Checkout uses: actions/checkout@v4 + - name: Setup Node + uses: actions/setup-node@v4 + with: + node-version: 22 + cache: npm + cache-dependency-path: site/package-lock.json + + - name: Install site dependencies + run: npm ci + working-directory: site + + - name: Build site + run: npm run build + working-directory: site + - name: Configure Pages uses: actions/configure-pages@v5 - name: Upload site artifact uses: actions/upload-pages-artifact@v3 with: - path: site + path: site/dist - name: Deploy to GitHub Pages id: deployment uses: actions/deploy-pages@v4 - diff --git a/.gitignore b/.gitignore index f2c43804..8dbceca8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .DS_Store export node_modules +site/dist diff --git a/README.md b/README.md index 92dc1375..e657216c 100644 --- a/README.md +++ b/README.md @@ -2,73 +2,126 @@ Languages: [English](README.md) | [Chinese](README.zh.md) -GDPevo is a public benchmark for evaluating self-evolving agents on -economically valuable, real business tasks. To our knowledge, it is the first -GDP-valued benchmark that treats agent evaluation as a stateful process: an -agent first works through related train tasks, turns experience into reusable -skills, and is then evaluated on held-out tasks from the same business -environment. +**GDPevo** is a public benchmark for measuring agent self-evolution on real +business work. -Most agent benchmarks still evaluate stateless task completion. GDPevo instead -asks whether agents can improve through experience: can they learn business -rules, source precedence, operating procedures, and output discipline from -earlier work, and can that learning make later work more accurate and cheaper -to execute? +The question is not only: *can the agent solve this task?* -The benchmark can be used to evaluate: +It is: **can the agent evolve across related tasks and get better on held-out +tasks from the same business world?** + +The first public release contains **120 GDP-worthy tasks** across **12 task +groups** in CRM, ERP, and Finance. Each task group contains one shared business +environment, **5 train tasks**, and **5 held-out test tasks**. Train tasks drive +the evolve step; test tasks measure whether the resulting update transfers to +later work it has not seen. + +## What GDPevo Measures + +Most agent benchmarks measure stateless task completion. GDPevo measures a +stateful loop: + +1. An agent works through related train tasks. +2. It turns train-task evidence into reusable procedures, memory, or operating rules. +3. It is evaluated on held-out tasks from the same business environment. + +This makes GDPevo useful for evaluating: - self-evolving or continual-learning agents; -- skill creators and skill optimizers; -- end-to-end agent memory systems. +- evolution-update and self-improvement mechanisms; +- end-to-end agent memory systems; +- whether self-evolution improves both **accuracy** and **cost**. -The first release contains 120 tasks organized into 12 task groups. Each task -group has one shared business environment, five train tasks, and five test -tasks. The task groups are constructed from economically meaningful industry -workflows, including finance, enterprise CRM, and ERP automation. +The benchmark is built around real company interfaces: CRM, ERP, finance, +procurement, support, lending, investment, HR, and reporting workflows. Task +groups are seeded from real-job sources such as GDPval and SOP-Bench, then +expanded into shared environments with lookalike records, hidden operational +rules, and deterministic rule-based graders. -In the released Codex GPT-5.5 xhigh run, evolved agents improve accuracy by -18.21 percentage points on average after inductive learning, while reducing -token cost by 25.75% on average. The released artifacts include executable task -groups, evaluation reports, generated skill packages, and a reusable evaluation -workspace that can automate the full scoring flow. +## Evaluation Setup -## Repository Layout +Released runs compare three modes: -| Path | Purpose | +| Mode | Meaning | | --- | --- | -| `data/` | Released task group data, data board, shared environments, train/test tasks, answers, evaluators, and data notes. | -| `experiments/` | Released evaluation protocol, evaluation workspaces, result reports, generated skills, and experiment board. | -| `site/` | GitHub Pages scaffold and public site assets. | -| `assets/` | Figures, logos, and visual assets used by released materials. | +| `base` | The agent solves the held-out test tasks cold, with no prior exposure to train tasks. | +| `demo` | The agent reads train tasks with gold answers, distills reusable procedures, then takes the test. | +| `reflect` | The agent attempts train tasks without answers, receives graded feedback, updates its procedure from mistakes, then takes the test. | + +Scores use `avg@3`: mean held-out accuracy over 3 attempts per task. + +Cost is reported in **USD** from raw token metrics in the report YAML files. +The released boards show token counts in thousands (`k`) and cost rounded to +two decimals. + +## Released Results + +Across all three harnesses, the same pattern holds: self-evolution improves +held-out accuracy by roughly **+17 to +22 percentage points**. + +| Harness | Model | Thinking level | `base` avg@3 | `demo` avg@3 | `reflect` avg@3 | Accuracy lift | Cost change | +| --- | --- | --- | ---: | ---: | ---: | ---: | ---: | +| Codex | GPT-5.5 | xhigh | 48.35% | 65.99% | 67.13% | +18.21 pp | -25.75% | +| Claude Code | Opus 4.8 | xhigh | 49.11% | 70.90% | 67.94% | +20.31 pp | -8.69% | +| Panofy | Opus 4.6 | high | 50.17% | 68.24% | 67.98% | +17.94 pp | +11.82% | + +Full per-task reports are under: + +- [`experiments/codex_gpt5_5_xhigh/`](experiments/codex_gpt5_5_xhigh/) +- [`experiments/claude_code_opus_4_8_xhigh/`](experiments/claude_code_opus_4_8_xhigh/) +- [`experiments/panofy_claude_opus_4_6_high/`](experiments/panofy_claude_opus_4_6_high/) + +The aggregate board is +[`experiments/EXPERIMENT_BOARD.md`](experiments/EXPERIMENT_BOARD.md). ## Data -Each task group contains one shared business environment, five train tasks, and -five test tasks. Train tasks provide the experience source, and test tasks -measure whether the resulting skills improve later work in the same business -environment. +Each task group contains: -Released task groups are summarized in [data/DATA_BOARD.md](data/DATA_BOARD.md). -See [data/README.md](data/README.md) for the data layout and task group format. +- one shared business environment; +- 5 train tasks; +- 5 held-out test tasks; +- answer files and deterministic evaluators; +- notes describing the data and grading setup. -## Experiments +Released task groups are summarized in +[`data/DATA_BOARD.md`](data/DATA_BOARD.md). See +[`data/README.md`](data/README.md) for the data layout and task group format. -The released evaluation run compares three conditions: +## Evaluation Workspaces + +Reusable evaluation workspaces live under +[`experiments/eval_workspace/`](experiments/eval_workspace/): + +| Workspace | Purpose | +| --- | --- | +| `codex/` | Codex evaluation workflow and guides | +| `claude_code/` | Claude Code evaluation workflow and guides | +| `panofy/` | Panofy evaluation workflow and guides | +| `codex_zh/`, `claude_code_zh/`, `panofy_zh/` | Chinese mirrors | -- `no_skill` -- `demonstration_skill` -- `reflection_skill` +Each workspace describes how to load its guides, run the full evaluation for a +task group, generate mode-specific artifacts when needed, aggregate `avg@3`, record +token/cost metrics, and write `report/.yaml`. + +## Repository Layout + +| Path | Purpose | +| --- | --- | +| `data/` | Released task group data, data board, shared environments, train/test tasks, answers, evaluators, and notes. | +| `experiments/` | Released evaluation protocols, reusable workspaces, report YAMLs, generated artifacts, and experiment board. | +| `site/` | React/Vite public site and GitHub Pages build pipeline. | -Results are summarized in -[experiments/EXPERIMENT_BOARD.md](experiments/EXPERIMENT_BOARD.md). +## Public Site -See [experiments/README.md](experiments/README.md) for details. +The public landing page and blog live in [`site/`](site/). The site is built +with React/Vite and deployed to GitHub Pages. -## Evaluation Workspace +Local preview: -The reusable evaluation workspace is available at -[experiments/eval_workspace/](experiments/eval_workspace/). A Chinese mirror is -available at [experiments/eval_workspace_zh/](experiments/eval_workspace_zh/). -The workspace describes how Codex can run the full evaluation workflow with -clean-context skill-generation and solver agents, aggregate `avg@3`, record -token and cost metrics, and write the final report. +```bash +cd site +npm ci +npm run build +npm run preview +``` diff --git a/README.zh.md b/README.zh.md index 76df5dd8..ecd505f2 100644 --- a/README.zh.md +++ b/README.zh.md @@ -2,49 +2,123 @@ 语言：[English](README.md) | [中文](README.zh.md) -GDPevo 是一个面向真实业务场景的公开 agent benchmark，用于评估自进化 agent 在具有经济价值的任务中学习和迁移规则的能力。据我们所知，它是首个面向 GDP-valued tasks 的 stateful benchmark：agent 先完成一组相关 train tasks，将经验沉淀为可复用 skill，再在同一业务环境下的 held-out test tasks 上接受评估。 +**GDPevo** 是一个公开 benchmark，用来在真实业务工作上衡量 agent 的 +self-evolution 能力。 -多数现有 agent benchmark 仍然评估“无状态”的单次任务完成。GDPevo 关注的是另一类能力：agent 能否从早期任务中学习业务规则、信息来源优先级、操作流程和输出规范，并让后续任务在准确率和执行成本上获得提升。 +我们不只问：*这个 agent 能不能解决这个任务？* -这个 benchmark 可以用于： +而是问：**它能不能借由相关任务完成自我进化，并在同一业务世界的 held-out 任务上变得更好？** -- 评估带有自进化或持续学习能力的 agent。 -- 评估 Skill Creator / SkillOpt 的能力。 -- 评估 Agent Memory 的端到端效果。 +首个公开版本包含 **120 个 GDP-worthy tasks**，覆盖 CRM、ERP 和 Finance +的 **12 个 task group**。每个 task group 包含一个共享业务环境、**5 个 +train tasks** 和 **5 个 held-out test tasks**。Train tasks 驱动 evolve； +test tasks 用来衡量这次更新能否迁移到它没见过的后续任务。 -首个公开版本包含 120 个任务，并组织为 12 个 task groups。每个 task group 包含一个共享业务环境、5 个 train tasks 和 5 个 test tasks。任务组从具有经济价值的产业工作流构造而来，例如金融、企业 CRM 和 ERP 自动化。 +## GDPevo 衡量什么 -在已发布的 Codex GPT-5.5 xhigh 运行中，自进化后的 agent 在归纳学习后平均获得 18.21 个百分点的准确率提升，同时 token 成本平均节省 25.75%。当前公开内容包括可执行 task groups、评估报告、生成的 skill packages，以及可复用的 evaluation workspace，用于自动执行完整评估流程并生成最终分数。 +多数 agent benchmark 衡量的是无状态的单次任务完成。GDPevo 衡量的是一个 +stateful loop： -## 仓库结构 +1. Agent 先处理一组相关 train tasks。 +2. 它把 train-task evidence 转成可复用的流程、memory 或操作规则。 +3. 它再在同一业务环境里的 held-out tasks 上接受评估。 -| 路径 | 用途 | +因此，GDPevo 可以用于评估： + +- self-evolving 或 continual-learning agents； +- evolution update 与自我改进机制； +- 端到端 agent memory systems； +- self-evolution 是否同时提升 **accuracy** 并降低 **cost**。 + +这个 benchmark 围绕真实公司 interface 构建，覆盖 CRM、ERP、finance、 +procurement、support、lending、investment、HR 和 reporting 等工作流。 +Task groups 由 GDPval 和 SOP-Bench 等真实工作来源 seed，再扩展成共享业务环境； +环境中包含 lookalike records、隐藏业务规则和 deterministic rule-based graders。 + +## 评估设置 + +已发布的 runs 比较三种 mode： + +| Mode | 含义 | | --- | --- | -| `data/` | 已发布的 task group 数据、数据看板、共享环境、train/test tasks、答案、评测器和数据说明。 | -| `experiments/` | 已发布的评估协议、evaluation workspaces、结果报告、生成 skills 和实验看板。 | -| `site/` | GitHub Pages 站点脚手架和公开展示资源。 | -| `assets/` | 已发布材料使用的图片、logo 和视觉资源。 | +| `base` | Agent 作为基线完成 held-out test tasks，不接触 train tasks。 | +| `demo` | Agent 阅读带 gold answers 的 train tasks，提炼可复用流程，再进入 test。 | +| `reflect` | Agent 先在没有答案的 train tasks 上尝试，收到 grading feedback，从错误中更新做法，再进入 test。 | + +分数使用 `avg@3`：每个 task 的 held-out accuracy 在 3 次尝试上的平均值。 + +Cost 使用 report YAML 中的原始 token metrics 计算，并以 **USD** 汇报。公开看板中 +token 数按千为单位 (`k`) 展示，cost 四舍五入到小数点后两位。 + +## 已发布结果 + +三个 harnesses 上都出现了相同趋势：self-evolution 能让 held-out accuracy 提升约 +**+17 到 +22 个百分点**。 + +| Harness | 模型 | Thinking level | `base` avg@3 | `demo` avg@3 | `reflect` avg@3 | 准确率提升 | Cost 变化 | +| --- | --- | --- | ---: | ---: | ---: | ---: | ---: | +| Codex | GPT-5.5 | xhigh | 48.35% | 65.99% | 67.13% | +18.21 pp | -25.75% | +| Claude Code | Opus 4.8 | xhigh | 49.11% | 70.90% | 67.94% | +20.31 pp | -8.69% | +| Panofy | Opus 4.6 | high | 50.17% | 68.24% | 67.98% | +17.94 pp | +11.82% | + +完整 per-task reports 位于： + +- [`experiments/codex_gpt5_5_xhigh/`](experiments/codex_gpt5_5_xhigh/) +- [`experiments/claude_code_opus_4_8_xhigh/`](experiments/claude_code_opus_4_8_xhigh/) +- [`experiments/panofy_claude_opus_4_6_high/`](experiments/panofy_claude_opus_4_6_high/) + +汇总看板见 +[`experiments/EXPERIMENT_BOARD.zh.md`](experiments/EXPERIMENT_BOARD.zh.md)。 ## 数据 -每个 task group 包含一个共享业务环境、5 个 train tasks 和 5 个 test tasks。Train tasks 提供经验来源，test tasks 用于衡量生成的 skills 是否能让同一业务环境下的后续任务获得提升。 +每个 task group 包含： + +- 一个共享业务环境； +- 5 个 train tasks； +- 5 个 held-out test tasks； +- 答案文件和 deterministic evaluators； +- 描述数据和 grading 设置的 notes。 -已发布 task groups 汇总在 [data/DATA_BOARD.zh.md](data/DATA_BOARD.zh.md)。 +已发布 task groups 汇总在 +[`data/DATA_BOARD.zh.md`](data/DATA_BOARD.zh.md)。数据目录说明和 task group +格式见 [`data/README.zh.md`](data/README.zh.md)。 -数据目录说明和 task group 格式见 [data/README.zh.md](data/README.zh.md)。 +## Evaluation Workspaces -## 实验 +可复用的 evaluation workspaces 位于 +[`experiments/eval_workspace/`](experiments/eval_workspace/)： -当前公开的评估运行比较三种条件： +| Workspace | 用途 | +| --- | --- | +| `codex/` | Codex evaluation workflow 和 guides | +| `claude_code/` | Claude Code evaluation workflow 和 guides | +| `panofy/` | Panofy evaluation workflow 和 guides | +| `codex_zh/`, `claude_code_zh/`, `panofy_zh/` | 中文镜像 | -- `no_skill` -- `demonstration_skill` -- `reflection_skill` +每个 workspace 都说明了如何加载 guides、为一个 task group 跑完整评估、在需要时生成 +mode-specific artifacts、聚合 `avg@3`、记录 token/cost metrics，并写出 +`report/.yaml`。 + +## 仓库结构 + +| 路径 | 用途 | +| --- | --- | +| `data/` | 已发布的 task group 数据、数据看板、共享环境、train/test tasks、答案、评测器和 notes。 | +| `experiments/` | 已发布的评估协议、可复用 workspaces、report YAMLs、生成 artifacts 和实验看板。 | +| `site/` | React/Vite public site 和 GitHub Pages build pipeline。 | +| `assets/` | 已发布材料使用的 figures、logos 和视觉资源。 | -结果汇总在 [experiments/EXPERIMENT_BOARD.zh.md](experiments/EXPERIMENT_BOARD.zh.md)。 +## Public Site -详细说明见 [experiments/README.zh.md](experiments/README.zh.md)。 +公开 landing page 和 blog 位于 [`site/`](site/)。站点使用 React/Vite 构建，并部署到 +GitHub Pages。 -## Evaluation Workspace +本地预览： -中文 evaluation workspace 位于 [experiments/eval_workspace_zh/](experiments/eval_workspace_zh/)，英文版本位于 [experiments/eval_workspace/](experiments/eval_workspace/)。它说明了如何使用 Codex 组织完整评估流程：生成 skill、运行 solver、聚合 `avg@3`、记录 token 和 cost 指标，并写出最终报告。 +```bash +cd site +npm ci +npm run build +npm run preview +``` diff --git a/assets/.gitkeep b/assets/.gitkeep deleted file mode 100644 index 8b137891..00000000 --- a/assets/.gitkeep +++ /dev/null @@ -1 +0,0 @@ - diff --git a/data/DATA_BOARD.md b/data/DATA_BOARD.md index 69c1eb9b..dc33f253 100644 --- a/data/DATA_BOARD.md +++ b/data/DATA_BOARD.md @@ -4,9 +4,10 @@ Languages: [English](DATA_BOARD.md) | [Chinese](DATA_BOARD.zh.md) This board summarizes the released GDPevo task groups: 120 tasks across 12 stateful benchmark units. Each task group has one shared business environment, -five train tasks, and five test tasks, allowing the benchmark to measure whether -experience from earlier work improves later work in the same environment. Full -definitions are available in each task group's `task_group.yaml`. +five train tasks, and five held-out test tasks, allowing the benchmark to +measure whether self-evolution from earlier tasks improves later work in the +same environment. Full definitions are available in each task group's +`task_group.yaml`. | task_group_id | scenario_id | domain | focus | train | test | path | | --- | --- | --- | --- | ---: | ---: | --- | diff --git a/data/DATA_BOARD.zh.md b/data/DATA_BOARD.zh.md index a7b5ff22..a755d307 100644 --- a/data/DATA_BOARD.zh.md +++ b/data/DATA_BOARD.zh.md @@ -2,7 +2,10 @@ 语言：[English](DATA_BOARD.md) | [中文](DATA_BOARD.zh.md) -本看板汇总当前公开的 GDPevo task groups：共 12 个 stateful benchmark 单元、120 个任务。每个 task group 包含一个共享业务环境、5 个 train tasks 和 5 个 test tasks，用于衡量早期任务经验是否能让同一环境下的后续任务获得提升。完整定义见各 task group 目录下的 `task_group.yaml`。 +本看板汇总当前公开的 GDPevo task groups：共 12 个 stateful benchmark 单元、120 +个任务。每个 task group 包含一个共享业务环境、5 个 train tasks 和 5 个 held-out +test tasks，用于衡量由早期任务驱动的 self-evolution 是否能让同一环境下的后续任务获得提升。 +完整定义见各 task group 目录下的 `task_group.yaml`。 | task_group_id | scenario_id | 领域 | 任务侧重点 | train | test | 路径 | | --- | --- | --- | --- | ---: | ---: | --- | diff --git a/data/README.md b/data/README.md index e4fe4897..6cf73e95 100644 --- a/data/README.md +++ b/data/README.md @@ -2,10 +2,10 @@ Languages: [English](README.md) | [Chinese](README.zh.md) -This directory contains the released GDPevo benchmark data: 120 tasks organized -into 12 task groups. Each task group packages one shared business environment, -five train tasks, five test tasks, reference answers, evaluators, and task -notes. +This directory contains the released GDPevo benchmark data: **120 GDP-worthy +tasks** organized into **12 task groups**. Each task group packages one shared +business environment, **5 train tasks**, **5 held-out test tasks**, reference +answers, deterministic evaluators, and task notes. ## Contents @@ -17,10 +17,14 @@ notes. ## Task Groups Each task group is a self-contained benchmark unit for stateful agent -evaluation. Train tasks provide related experience inside a real business -environment; test tasks measure whether the generated skill improves later work -in the same environment. The released task groups cover economically meaningful -workflows such as finance, enterprise CRM, and ERP automation. +evaluation. Train tasks drive the evolve step inside one business environment; +held-out test tasks measure whether the resulting update transfers to later +work it has not seen. + +The released task groups cover real company interfaces across CRM, ERP, and +Finance. They are seeded from real-job sources such as GDPval and SOP-Bench, +then expanded into shared environments with lookalike records, recurring +operational rules, and deterministic rule-based graders. ## Directory Layout @@ -63,10 +67,10 @@ task_groups/ | Path | Purpose | | --- | --- | | `task_groups/*/task_group.yaml` | Task group index with metadata, task paths, environment files, and scoring goals. | -| `task_groups/*/env/` | Shared business environment used by all train and test tasks in the task group. | +| `task_groups/*/env/` | Shared business environment used by all train and held-out test tasks in the task group. | | `task_groups/*/env/README.md` | Environment setup and access instructions, when provided. | | `task_groups/*/train_tasks/*/input/prompt.txt` | The task prompt shown to the agent for a train task. | -| `task_groups/*/test_tasks/*/input/prompt.txt` | The task prompt shown to the agent for a test task. | +| `task_groups/*/test_tasks/*/input/prompt.txt` | The task prompt shown to the agent for a held-out test task. | | `task_groups/*/*/input/payloads/` | Task-local input files, including answer templates and any task-specific request materials. | | `task_groups/*/*/input/payloads/answer_template.json` | Expected answer schema for the task, when provided. | | `task_groups/*/*/output/answer.json` | Reference answer used for evaluation. | @@ -77,6 +81,6 @@ task_groups/ When running an evaluation, the solver agent should receive only the task input, the allowed environment access exposed by the evaluation harness or task group -metadata, and any skill file required by the evaluation condition. Reference +metadata, and any mode-specific artifact required by the evaluation mode. Reference answers, evaluator files, and task notes are for evaluation, inspection, and benchmark documentation. diff --git a/data/README.zh.md b/data/README.zh.md index 07e0e5c6..69009d61 100644 --- a/data/README.zh.md +++ b/data/README.zh.md @@ -2,7 +2,9 @@ 语言：[English](README.md) | [中文](README.zh.md) -本目录包含已发布的 GDPevo benchmark 数据：120 个任务，组织为 12 个 task groups。每个 task group 包含一个共享业务环境、5 个 train tasks、5 个 test tasks、参考答案、评测器和任务说明。 +本目录包含已发布的 GDPevo benchmark 数据：**120 个 GDP-worthy tasks**，组织为 +**12 个 task group**。每个 task group 包含一个共享业务环境、**5 个 train +tasks**、**5 个 held-out test tasks**、参考答案、确定性评测器和任务说明。 ## 内容 @@ -13,7 +15,13 @@ ## Task Groups -每个 task group 都是一个用于 stateful agent evaluation 的自包含 benchmark 单元。Train tasks 提供同一真实业务环境中的相关经验，test tasks 用于衡量生成的 skill 是否能让后续任务获得提升。当前公开 task groups 覆盖金融、企业 CRM、ERP 自动化等具有经济价值的工作流程。 +每个 task group 都是一个用于 stateful agent evaluation 的自包含 benchmark 单元。 +Train tasks 在同一业务环境中驱动 evolve；held-out test tasks 用来衡量生成的 +更新与规则能否迁移到 agent 没见过的后续任务。 + +当前公开 task groups 覆盖 CRM、ERP 和 Finance 中的真实公司 interface。它们由 +GDPval 和 SOP-Bench 等真实工作来源 seed，再扩展成共享业务环境；环境中包含 +lookalike records、反复出现的业务规则和确定性的 rule-based graders。 ## 目录结构 @@ -56,10 +64,10 @@ task_groups/ | 路径 | 用途 | | --- | --- | | `task_groups/*/task_group.yaml` | task group 索引文件，记录元信息、任务路径、环境文件和评分目标。 | -| `task_groups/*/env/` | 该 task group 内所有 train 和 test tasks 共用的业务环境。 | +| `task_groups/*/env/` | 该 task group 内所有 train 和 held-out test tasks 共用的业务环境。 | | `task_groups/*/env/README.md` | 环境启动和访问说明；如果该环境提供了此文件。 | | `task_groups/*/train_tasks/*/input/prompt.txt` | train task 展示给 agent 的任务 prompt。 | -| `task_groups/*/test_tasks/*/input/prompt.txt` | test task 展示给 agent 的任务 prompt。 | +| `task_groups/*/test_tasks/*/input/prompt.txt` | held-out test task 展示给 agent 的任务 prompt。 | | `task_groups/*/*/input/payloads/` | 该任务的本地输入文件，包括答案模板以及该任务专用的请求材料。 | | `task_groups/*/*/input/payloads/answer_template.json` | 该任务期望的答案格式；如果该任务提供了此文件。 | | `task_groups/*/*/output/answer.json` | 用于评测的参考答案。 | @@ -68,4 +76,6 @@ task_groups/ ## 使用说明 -运行评估时，solver agent 应该只接收任务输入、evaluation harness 或 task group 元信息暴露出来的可用环境入口，以及当前评估条件所要求的 skill 文件。参考答案、评测器文件和任务 notes 主要用于评测、检查和 benchmark 文档说明。 +运行评估时，solver agent 应该只接收任务输入、evaluation harness 或 task group +元信息暴露出来的可用环境入口，以及当前 evaluation mode 所要求的特定 artifact。参考答案、 +评测器文件和任务 notes 主要用于评测、检查和 benchmark 文档说明。 diff --git a/experiments/EXPERIMENT_BOARD.md b/experiments/EXPERIMENT_BOARD.md index b101ff32..07efec33 100644 --- a/experiments/EXPERIMENT_BOARD.md +++ b/experiments/EXPERIMENT_BOARD.md @@ -5,16 +5,16 @@ Languages: [English](EXPERIMENT_BOARD.md) | [Chinese](EXPERIMENT_BOARD.zh.md) This board summarizes the released evaluation results for GDPevo's public benchmark runs. The released run compares a stateless `base` baseline with two evolve methods, `demo` and `reflect`, to -measure whether agent experience improves later GDP-valued business tasks. +measure whether agent self-evolution improves later GDP-valued business tasks. Full structured reports are stored under released experiment directories, such as `codex_gpt5_5_xhigh/reports/`, `claude_code_opus_4_8_xhigh/reports/`, and `panofy_claude_opus_4_6_high/reports/`. -In the released Codex GPT-5.5 xhigh run, skill-based evolution improves accuracy +In the released Codex GPT-5.5 xhigh run, self-evolution improves accuracy by 18.21 percentage points on average and reduces token cost by 25.75% on average. -Efficiency metrics only count test solver subagent answer-writing. They are averaged across the 3 attempts for each test task first, then averaged across the 5 test tasks. Skill generation, environment startup, evaluator execution, and main-agent aggregation are outside these metrics. +Efficiency metrics only count test solver subagent answer-writing. They are averaged across the 3 attempts for each test task first, then averaged across the 5 test tasks. Artifact generation, environment startup, evaluator execution, and main-agent aggregation are outside these metrics. This board displays `avg@3` as a percentage and token metrics in thousands (`k`). Cost is calculated from the raw token counts in the report YAML files, then rounded to two decimals for display. @@ -83,7 +83,7 @@ cost_USD_avg_3 = ## Claude Code (Opus 4.8, xhigh) -In the released Claude Code Opus 4.8 xhigh run, skill-based evolution improves +In the released Claude Code Opus 4.8 xhigh run, self-evolution improves accuracy by 20.31 percentage points on average and reduces token cost by 8.69% on average (averaged over the two methods across the 12 task groups). @@ -151,7 +151,7 @@ The Claude Code token columns are the four Anthropic usage buckets (uncached inp ## Panofy (Claude Opus 4.6, high) -In the released Panofy Claude Opus 4.6 high run, skill-based evolution improves accuracy by 17.94 percentage points on average and increases token cost by 11.82% on average (averaged over the two methods across the 12 task groups). +In the released Panofy Claude Opus 4.6 high run, self-evolution improves accuracy by 17.94 percentage points on average and increases token cost by 11.82% on average (averaged over the two methods across the 12 task groups). For the released `claude-opus-4-6, high` run, this board uses the Claude Opus 4.6 prices for the three token buckets exposed by Panofy: diff --git a/experiments/EXPERIMENT_BOARD.zh.md b/experiments/EXPERIMENT_BOARD.zh.md index 4894cedc..f66eaa2e 100644 --- a/experiments/EXPERIMENT_BOARD.zh.md +++ b/experiments/EXPERIMENT_BOARD.zh.md @@ -2,11 +2,11 @@ 语言：[English](EXPERIMENT_BOARD.md) | [中文](EXPERIMENT_BOARD.zh.md) -本看板汇总 GDPevo 公开 benchmark 运行的已发布评估结果。当前运行将无状态 `base` baseline 与 `demo`、`reflect` 两种 evolve 方法进行对比，用于衡量 agent 经验是否能提升后续具有经济价值的业务任务。完整结构化 reports 存放在已发布实验目录下，例如 `codex_gpt5_5_xhigh/reports/`。 +本看板汇总 GDPevo 公开 benchmark 运行的已发布评估结果。当前运行将无状态 `base` baseline 与 `demo`、`reflect` 两种 evolve 方法进行对比，用于衡量 agent self-evolution 是否能提升后续具有经济价值的业务任务。完整结构化 reports 存放在已发布实验目录下，例如 `codex_gpt5_5_xhigh/reports/`。 -在已发布的 Codex GPT-5.5 xhigh 运行中，skill-based evolution 平均带来 18.21 个百分点的准确率提升，并让 token 成本平均下降 25.75%。 +在已发布的 Codex GPT-5.5 xhigh 运行中，self-evolution 平均带来 18.21 个百分点的准确率提升，并让 token 成本平均下降 25.75%。 -效率指标只统计 test solver agent 写答案的过程。计算时先对每个 test task 的 3 次 attempts 取平均，再对 5 个 test tasks 取平均。Skill 生成、环境启动、evaluator 执行和主 agent 汇总不计入这些指标。 +效率指标只统计 test solver agent 写答案的过程。计算时先对每个 test task 的 3 次 attempts 取平均，再对 5 个 test tasks 取平均。Artifact 生成、环境启动、evaluator 执行和主 agent 汇总不计入这些指标。 看板中的 `avg@3` 以百分比展示，token 指标以 `k` 为单位展示。Cost 仍使用 report YAML 中的原始 token 数计算，并在看板中四舍五入到两位小数展示。 @@ -74,7 +74,7 @@ cost_USD_avg_3 = ## Claude Code (Opus 4.8, xhigh) -在已发布的 Claude Code Opus 4.8 xhigh 运行中，skill-based evolution 平均带来 20.31 个百分点的准确率提升，并让 token 成本平均下降 8.69%（对两种 evolve 方法、跨 12 个 task group 取平均）。 +在已发布的 Claude Code Opus 4.8 xhigh 运行中，self-evolution 平均带来 20.31 个百分点的准确率提升，并让 token 成本平均下降 8.69%（对两种 evolve 方法、跨 12 个 task group 取平均）。 对于已发布的 `claude-opus-4-8, xhigh` 运行，本看板使用 Claude Opus 4.8 价格： diff --git a/experiments/README.md b/experiments/README.md index ba7dcc18..f970e544 100644 --- a/experiments/README.md +++ b/experiments/README.md @@ -5,7 +5,8 @@ Languages: [English](README.md) | [Chinese](README.zh.md) This directory contains released GDPevo evaluation methods and result artifacts. The evaluation compares a stateless baseline with two evolve methods: `demo` and `reflect`. It can be used to study -self-evolving agents, skill creators, and end-to-end agent memory systems. +self-evolving agents, evolution-update mechanisms, and end-to-end agent memory +systems. ## Contents @@ -19,4 +20,4 @@ self-evolving agents, skill creators, and end-to-end agent memory systems. | `panofy_claude_opus_4_6_high/` | Released Panofy Claude Opus 4.6 high evaluation run | Each released experiment directory contains a `config.yaml`, structured report -YAML files, and any generated skill packages referenced by those reports. +YAML files, and any generated artifacts referenced by those reports. diff --git a/experiments/README.zh.md b/experiments/README.zh.md index 7a52a5e7..32109dac 100644 --- a/experiments/README.zh.md +++ b/experiments/README.zh.md @@ -2,7 +2,7 @@ 语言：[English](README.md) | [中文](README.zh.md) -本目录包含已发布的 GDPevo 评估方法和结果产物。评估将无状态 baseline 与两种 evolve 方法进行对比：`demo` 和 `reflect`。它可以用于观察自进化 agent、Skill Creator / SkillOpt，以及 Agent Memory 的端到端效果。 +本目录包含已发布的 GDPevo 评估方法和结果产物。评估将无状态 baseline 与两种 evolve 方法进行对比：`demo` 和 `reflect`。它可以用于观察自进化 agent、evolution update 机制，以及 Agent Memory 的端到端效果。 ## 内容 @@ -15,4 +15,4 @@ | `claude_code_opus_4_8_xhigh/` | 已发布的 Claude Code Opus 4.8 xhigh 评估运行 | | `panofy_claude_opus_4_6_high/` | 已发布的 Panofy Claude Opus 4.6 high 评估运行 | -每个已发布实验目录都包含 `config.yaml`、结构化 report YAML 文件，以及这些 reports 引用的生成 skill packages（如果有）。 +每个已发布实验目录都包含 `config.yaml`、结构化 report YAML 文件，以及这些 reports 引用的生成 artifacts（如果有）。 diff --git a/site/README.md b/site/README.md index 0aee5731..4fdfce91 100644 --- a/site/README.md +++ b/site/README.md @@ -1,12 +1,15 @@ # GDPevo Site -This directory contains the static GitHub Pages scaffold for GDPevo. +This directory contains the React/Vite GitHub Pages site for GDPevo. -The site is intentionally dependency-free: GitHub Pages publishes the files in -this directory directly. +The landing page is rendered by React and built into `dist/` before deployment. +`blog.html` remains a static article file and is copied into the build output. ## Contents | Path | Purpose | | --- | --- | +| `src/` | React components and page data for the landing page. | | `styles.css` | Shared site styling. | +| `blog.html` | Static blog article copied into `dist/` during build. | +| `scripts/postbuild.mjs` | Copies the static blog, assets, and `.nojekyll` into `dist/`. | diff --git a/site/assets/gdpevo-pipeline-backup.png b/site/assets/gdpevo-pipeline-backup.png deleted file mode 100644 index bfeae861..00000000 Binary files a/site/assets/gdpevo-pipeline-backup.png and /dev/null differ diff --git a/site/assets/gdpevo-pipeline-v2-backup.png b/site/assets/gdpevo-pipeline-v2-backup.png deleted file mode 100644 index ce84eb65..00000000 Binary files a/site/assets/gdpevo-pipeline-v2-backup.png and /dev/null differ diff --git a/site/assets/gdpevo-pipeline.png b/site/assets/gdpevo-pipeline.png index 6af047ce..4199f5df 100644 Binary files a/site/assets/gdpevo-pipeline.png and b/site/assets/gdpevo-pipeline.png differ diff --git a/site/blog.html b/site/blog.html index 786ef8d2..4d849a02 100644 --- a/site/blog.html +++ b/site/blog.html @@ -3,323 +3,32 @@ - GDPevo Blog — Measuring Agent Self-Evolution on Real Business Work - - - + GDPevo - Evaluating Agent Self-Evolution on Real Business Tasks + + + - - - - -
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- Measuring Agent Self-Evolution on Real Business Work - 在真实业务工作上衡量 Agent 的自我进化 -

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- In the AI era, once something can be evaluated and that evaluation can be automated, the problem is essentially solved. Better evaluation begets better agents; better agents in turn drive sharper evaluation — and the loop converges fast. - 在 AI 时代，一件事如果能被评估，并且评估可以被自动化，那它基本上就已经被解决了。更好的评估孕育更好的 agent，更好的 agent 又反过来推动更精细的评估——这个循环收敛得很快。 -

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- Self-evolution — agents that rewrite their own skills, memory, or harness to get better at a class of tasks over time — is a problem the field clearly cares about. Companies built around agentic self-improvement (Cognition, Reflection AI, and others) have raised on the order of billions of dollars at multi-billion-dollar valuations in 2025 alone. Yet on real productivity work — auditing AP invoices, reconciling event sponsorship across CRM and finance, building a branch close-out package — there is essentially no benchmark for whether an agent can actually self-evolve. It can't be measured today, let alone measured automatically. - Self-evolution——agent 通过自己改写 skill、memory 或 harness，在一类任务上越做越好——是一个业界明显在乎的问题。围绕 agent 自我进化的公司（Cognition、Reflection AI 等）仅 2025 一年就以数十亿美元估值募了约 数十亿美元的资金。然而在真实生产力工作这一面——审核 AP 发票、把展会赞助在 CRM 和财务两边对账、做一个分行月度结账包——几乎没有 benchmark能告诉你 agent 到底有没有 self-evolve。今天它根本测不了，更谈不上自动化测。 -

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- GDPevo fills that gap. It is both a process for automatically building self-evolution benchmarks on real-job tasks, and a released benchmark produced by that process — twelve task groups across CRM, ERP, and Finance, each with five train tasks and five test tasks under a shared environment and rule-based graders. The construction-and-evaluation pipeline is shown below. - GDPevo 就是来补这个 gap 的。它既是一套流程——在真实业务任务上自动化地构建 self-evolution benchmark；也是这套流程的产物，一份已发布的 benchmark——12 组 task group，覆盖 CRM、ERP、Finance，每组 5 个 train + 5 个 test，共享同一个环境，用基于规则的 grader 打分。构建与评估的整体流程如下图所示。 -

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- Automating the construction and evaluation of productivity-flavored datasets has two hard problems on the construction side. First, train and test must be cleanly separated so the agent can actually learn from train and have it show up on test — many existing benchmarks fail this and end up training and testing on the same set. We solve it with a method we call Hidden Rules (working name): operational rules are planted across the train tasks in pieces and recombined on the test, so a passing score requires real abstraction, not memorization. Second, large models are lazy — they declare victory while quietly skipping work. We add a review mechanism: a panel of independent reviewer agents audits each task group end-to-end, and a group only ships when the reviewers confirm everything was actually delivered. - 在生产力相关的数据集上做自动化构建与评估，构建侧有两大难点。第一，必须把 train 和 test 干净地分开，让模型真的能从 train 里学到东西，并且在 test 上体现出来——很多现有 benchmark 就是没做到这点，最后在 train 上训、在 train 上测。我们用一个叫 Hidden Rules（暂定名）的方法来解：把业务规则拆散埋进 train，再在 test 上重新组合，要拿分就必须真的把规则抽象出来，而不是死记硬背。第二，大模型会偷懒——经常嘴上说"做完了"实际跳过了步骤。我们加了一套 review 机制：由若干独立的 reviewer agent 端到端审计每一组任务，所有内容确认交付了，这一组才会放行。 -

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- In addition, our evaluation makes three commitments. One, grading is rule-based, not LLM-as-a-judge — every score is reproducible and every failure points to a specific rule. Two, both cost and accuracy are first-class citizens — a useful agent isn't just one that gets the right answer, it's one that gets there more cheaply over time. Three, the workspace is natural-language driven — you describe the experiment and the chart you want in one sentence, and a coding agent generates the analysis on the fly; you don't write code yourself. - 在评估这一面，我们坚持三件事。第一，规则化打分，不用 LLM-as-a-judge——每一个分都是可复现的，每一次失败都能定位到具体的规则。第二，cost 与 accuracy 同为一等公民——一个好用的 agent 不仅要答得对，还要随时间推移、用更少的代价把题做出来。第三，工作区由自然语言驱动——你用一句话描述要跑的实验和想要的图表，一个 coding agent 当场生成分析代码出图；你自己不需要写代码。 -

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- We close with evaluation and findings. On all three agent harnesses we tested, self-evolution lifts held-out accuracy by +17 to +22 points; on two of them token usage actually goes down, not up — fluency, not just a higher score. Below, we walk through how this benchmark is built, and how it is used. - 最后是 evaluation 和 findings。在我们测的三套 agent harness 上，self-evolution 都把 held-out 准确率提升了 +17 到 +22 个百分点；其中两套 harness 上 token 消耗不增反降——是变熟练，不止是分数变高。下面我们就来看看这个 benchmark 是怎么被构建出来的，以及怎么使用。 -

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How we built it.我们怎么构建。

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- Building a self-evolution benchmark for real business work poses two challenges. First, the construction should be agent-driven end-to-end — humans write the procedure once, agents do the rest. This matters for two reasons. It helps the benchmark outrun data leakage: as long as agents can spawn fresh tasks faster than models absorb the leaked ones, the benchmark stays ahead. It also lets the benchmark scale: when agents both build and evaluate, the loop is closed — no human bottleneck — and the benchmark can keep growing on its own. Second, train and test have to be related but not redundant: the train tasks must teach a real, hidden lesson that pays off only when the lesson is extracted, not memorized. - 为真实业务工作构建一个 self-evolution benchmark，有两个 challenge。第一，构建过程应该是 agent 端到端跑出来的——人只把流程写一次，剩下交给 agent。这件事之所以重要有两个原因。它能帮 benchmark 跑赢数据泄露：只要 agent 生成新题的速度比模型吸收已泄露题的速度快，benchmark 就一直保持领先。它也让 benchmark 能真正 scale：当 agent 既负责构造、又负责评测，整个回路就闭合了——没有人力瓶颈，左脚踩右脚，benchmark 可以自己长大。第二，train 和 test 要相关、但不能冗余：train 必须真的"教"一个隐藏的规则，这个规则只有被抽象出来才会在 test 上派上用场，而不是被死记下来。 -

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Built end-to-end by agents.从头到尾，全部由 agent 完成。

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Humans wrote the pipeline once. After that, agents do everything (illustrated below). They pull seed scenarios from public real-job benchmarks (GDPVal, SOP-Bench, JobBench) and spawn many candidate task groups from those seeds. For each group, they build a shared environment and author 5 train + 5 test tasks with rule-based evaluators. A calibrator then tunes difficulty so that an evolution setup clearly beats a no-evolution setup — this filters out tasks where evolution wouldn't change the answer, keeping the benchmark focused on agents that actually have to learn from experience. Finally, six independent reviewer agents audit the result, and a group ships only when 5 of 6 pass — these reviewers exist because agents have a habit of declaring victory while quietly skipping work, so completeness, file presence, and planted hidden rules are exactly what gets checked. Twelve task groups make it through this filter and form the public release. - 人只写一次 pipeline，之后全靠 agent（如下图所示）。先从公开的真实业务 benchmark（GDPVal、SOP-Bench、JobBench）里取种子场景，再据此 spawn 出大量候选 task group。每组里搭一个共享 env，写 5 个 train + 5 个 test 任务并配上基于规则的 evaluator。然后 calibrator 调难度，让 evolution 设定明显超过 no-evolution 设定——这一步是为了把那些"是否进化都不影响答案"的题筛掉，让 benchmark 集中在那些真的需要从经验里学东西的 agent 上。最后由 6 个互相独立的 reviewer agent 审核，6 个里 5 个通过这一组才放行——之所以要 reviewer，是因为 agent 经常嘴上说"做完了"、实际偷懒，所以审核的就是结构、完整性、文件是否齐全、隐藏规则是否真的埋了进去。最终通过这层筛选活下来、进入公开发布的，是 12 组 task group。

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- GDPevo data pipeline: seed scenarios → multi-agent task factory → quality review → release. -
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Hidden rules, spread across the train tasks.隐藏规则，分散到各个 train task 里。

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For every task group we plant a small set of hidden operational rules. A CRM lead-capture group might encode a sponsor-status precedence rule (finance invoices outrank badge scans) and a suppression-list policy (don't contact certain segments). A procurement-readiness group enforces that open vendor-risk events and AP holds force a "held" line, regardless of how clean the PO looks. - 每个 task group 我们都会埋一小组隐藏的业务规则。CRM 销售线索组可能埋了两条：赞助商身份的优先级（财务发票优先于胸卡扫描），以及 suppression list 政策（某些细分人群禁止联系）。采购就绪组则要求：只要供应商有 open 的 risk event 或 AP hold，对应行就必须 held，不管 PO 看起来多干净。

-

We spread these rules across the 5 train tasks, so each train task only exercises a subset. The 5 test tasks are deliberately built as combinations — say, the precedence rule together with the suppression policy. An agent that solves each train in isolation learns the rules in pieces. An agent that distills them into a single reusable skill has them all in one place — and when the test asks for two at once, it doesn't need to rediscover them. That's why a higher test score is real evidence of learning, not luck. - 我们把这些规则分散到 5 个 train 里，每个 train 只触发一部分。5 个 test 故意被设计成这些规则的组合——比如同时触发"优先级 + suppression"。只盯着单个 train 做的 agent 学到的是碎片；把这些规则整理进一份可复用 skill 的 agent 则把它们放在一处——test 同时问两条时，它不需要重新发现。这就是 test 分数变高的原因，是真的"学到了"，而不是运气。

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How to evaluate with it.怎么用它做评估。

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- Two things we want from an evaluation harness: it should grade in a way you can audit; and it should treat cost as a first-class citizen alongside accuracy. Each one motivated a specific design. - 我们对评估 harness 有两个要求：评分必须可被人审计；cost 要和 accuracy 同等重要。下面两块就是为了满足这两个要求。 -

- -

Rule-based grading, not "ask another LLM".基于规则的评分，而不是"再叫一个 LLM 来判"。

-
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GDPevo grades with deterministic, rule-based checkers — for example, was the right set of records returned, did the amount round to the right precision. Two things follow. First, the score is reproducible: the same answer always gets the same grade, regardless of who runs it or when. Second, every failure is traceable: instead of a vague verdict, you see exactly which rule was violated and by how much. That trace is what makes the benchmark useful for diagnosis — you can read it back to find weak spots in your agent and feed those weak spots into the next round of skill or memory updates. - GDPevo 用确定性的 rule-based checker 打分——比如返回的记录集对不对、金额是否遵守了要求的精度。这带来两个好处。第一，评分是可复现的：同一份答案，谁跑、什么时候跑，得到的分都一样。第二，每一次失败都是可追溯的：你看到的不是一个含糊的整体结论，而是具体哪一条规则被违反、扣了多少分。这种可追溯性让 benchmark 成为诊断工具——你可以反过来读这些 trace，找到你的 agent 短板在哪儿，再把这些短板喂回下一轮的 skill 或 memory 更新。

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Cost and accuracy, both first-class.cost 和 accuracy 都是一等公民。

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A useful agent isn't just one that gets the right answer — it's one that stops redoing the same legwork every time a similar task comes in. Self-evolution should look like a human getting fluent: more accurate and faster, with fewer tokens, fewer steps, and cleaner moves. So we instrument every run end-to-end: per-agent total token spend, plus a breakdown by reasoning, tool calls, and stage. That observability isn't only for our analysis — those traces also become the raw material the agent itself can learn from. - 一个有用的 agent 不仅要答得对，还得不再每次都把同样的活儿重做一遍。Self-evolution 应该像人变熟练：更准、更快，用更少的 token、更少的步数、更干净的做法。所以我们对每一次运行都做端到端打点：每个 agent 的总 token 消耗，以及按 reasoning、工具调用、不同阶段的 breakdown。这套 observability 不只服务我们自己的分析——这些 trace 本身也是 agent 可以拿去 self-evolve 的学习材料。

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Evaluations and findings.实测与结果。

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Every run below was driven by natural language: we pointed a coding agent (Codex or Claude Code) at the evaluation workspace — a folder of plain Markdown guides, prompts, and directory conventions — typed one sentence describing the experiment and the chart we wanted, and the agent generated the analysis code, called the graders, and wrote the report. No hand-written harness, no SDK to learn. - 下面这些实验都是用自然语言驱动跑出来的：我们把一个 coding agent（Codex 或 Claude Code）指向评估工作区——一个装满纯 Markdown 指南、prompt 和目录约定的文件夹——用一句话说清要跑什么实验、想要什么样的图，agent 就会自己生成分析代码、调 grader、写 report。没有手写的 harness，也没有要学的 SDK。

-

We ran the same 12 task groups under three settings, on three different agent harnesses: - 我们在同样的 12 组 task group 上跑了三种设定，跨三套不同的 agent harness：

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  • base — the agent solves the 5 test tasks cold, with no prior exposure to the 5 train tasks. - base——agent 直接做 5 个 test，没接触过 5 个 train。
    • -
  • demo — the agent reads the 5 train tasks with their gold answers first, distills skills/memory from them, and then takes the test. (Analogous to SFT.) - demo——agent 先读 5 个 train 的题目和标准答案，从中沉淀 skill/memory，再去做 test。（类似 SFT。）
    • -
  • reflect — the agent attempts the 5 train tasks without seeing answers, gets back graded reward and feedback, updates its skills/memory from what it got wrong, then takes the test. (Analogous to RL.) - reflect——agent 看不到答案，自己做 5 个 train，事后拿到 reward 和反馈，改自己的 skill/memory，再去做 test。（类似 RL。）
    • -
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- Codex · GPT-5.5Codex · GPT-5.5 - xhigh · avg@3 over 12 task groupsxhigh · 12 个 task group 的 avg@3 -
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- Mode模式 - avg@3Tokens (k)Tokens (k)USD -
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base48.35%709.21.08
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demo65.99%466.10.81
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reflect67.13%458.70.79
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- Claude Code · Opus 4.8Claude Code · Opus 4.8 - xhigh · avg@3 over 12 task groupsxhigh · 12 个 task group 的 avg@3 -
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- Mode模式 - avg@3Tokens (k)Tokens (k)USD -
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base49.11%376.80.61
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demo70.90%340.80.55
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reflect67.94%331.90.56
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- Panofy · Opus 4.6Panofy · Opus 4.6 - high · avg@3 over 12 task groupshigh · 12 个 task group 的 avg@3 -
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- Mode模式 - avg@3Tokens (k)Tokens (k)USD -
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base50.17%318.90.72
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demo68.24%383.50.80
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reflect67.98%365.20.79
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- The shape is the same on all three harnesses: skills lift held-out accuracy by ~17–22 points, and on the GPT-5.5 / Opus 4.8 setups tokens go down, not up — the fluency story, not just a higher score. On one task group (operational financial modeling), Codex went from 42.76% to 92.47% with fewer tokens than cold start; on the same group, Claude Code's demo reached 100%, up from 51.76%. - 三套 harness 形状一致：skill 让 held-out 准确率提升 约 17–22 个百分点，且在 GPT-5.5 / Opus 4.8 这两套上，花的 token 反而更少——这正是"变熟练"，而不仅仅是分数更高。在 operational financial modeling 这一组上，Codex 从 42.76% 升到 92.47%，token 比冷启动还少；同一组上，Claude Code 的 demo 直接到了 100%，起点是 51.76%。 -

-

See the per-group breakdown on the homepage →在首页查看逐组的明细 →

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GDPevo is a seed, not a finished product.GDPevo 是一颗种子，不是一件成品。

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The tasks, environments, graders, generated skills, and full reports are open. Bring your own - agents and scenarios. Submit harder hidden rules. The goal is not a leaderboard, but a public interface where agents - that actually do productive work can be trained — and where the evidence of that improvement is open to - inspection. - 任务、环境、grader、生成的 skill 包、完整 report 都是开放的。欢迎带上你自己的 agent、你自己的业务场景，欢迎来挑战、欢迎提交更难的隐藏规则。 - 我们的目标不是一张 leaderboard，而是一个公共接口 —— 让真正能干生产力工作的 agent 在这里被训练出来，并且这种"它真的变好了"的证据可以被任何人审计。

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- GitHub - Experiment board实验看板 - Eval workspace评估工作区 -
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- - 
@misc{gdpevo2026,
-  title  = {GDPevo: Measuring Agent Self-Evolution on Real Business Work},
-  author = {PrismShadow team},
-  year   = {2026},
-  url    = {https://github.com/Prism-Shadow/GDPevo}
-}
-
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- -
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GDPevo
-

Measuring agent self-evolution on GDP-worthy work.在真实业务工作上衡量 Agent 的自我进化。

- github.com/Prism-Shadow/GDPevo -
-
- - +
+ diff --git a/site/index.html b/site/index.html index 2da4f461..5e92464b 100644 --- a/site/index.html +++ b/site/index.html @@ -3,610 +3,32 @@ - GDPevo — Measuring Agent Self-Evolution on Real Business Work - - - + GDPevo - Evaluating Agent Self-Evolution on Real Business Tasks + + + - - -
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- GDPevo: Measuring Agent
Self-Evolution on Real Business Work - GDPevo：在真实业务工作上
衡量 Agent 的自我进化 -

-

- Not can the agent solve this task?
— but can it learn from related tasks to get better? - 不是问这个 agent 能不能解决这个任务，
而是问它能不能通过相关任务的经验，越来越擅长这一类任务。 -

-

- Public Benchmark Release · 2026 · PrismShadow team - 公开基准发布 · 2026 · PrismShadow 团队 -

-
- - - GitHub - - - - Results结果 - - - - Read the blog阅读博客 - - - - Experiment board实验看板 - -
- -
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- 120 - GDP-worthy tasks真实业务任务 -
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- 12 - Task groups · CRM · ERP · Finance任务组 · CRM · ERP · Finance -
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- TBD暂定 - Held-out accuracy lift from skillsskill 带来的 held-out 准确率提升 -
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- TBD暂定 - Token cost — fewer, not moretoken 成本 —— 更少，而非更多 -
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- Skills make agents more accurate while spending fewer tokens. - Skill 让 agent 更准,同时花更少的 token。 -

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- Each group has 5 train and 5 held-out test tasks. We compare a cold-start baseline - (base) against two skill-writers — demo (learn from worked examples) and - reflect (learn from its own mistakes) — and measure the lift on tasks the skill-writer never saw. - 每组有 5 个 train、5 个 held-out test。我们用冷启动基线（base）对比两种 skill-writer —— - demo（从样例中学）和 reflect（从自己的错误中学）—— 衡量在 skill-writer 没见过的任务上的提升。 -

- -
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- Codex · GPT-5.5Codex · GPT-5.5 - xhigh · avg@3 over 12 task groupsxhigh · 12 个 task group 的 avg@3 -
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- Mode模式 - avg@3Tokens (k)Tokens (k)USD -
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base48.35%709.21.08
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demo65.99%466.10.81
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reflect67.13%458.70.79
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- Claude Code · Opus 4.8Claude Code · Opus 4.8 - xhigh · avg@3 over 12 task groupsxhigh · 12 个 task group 的 avg@3 -
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- Mode模式 - avg@3Tokens (k)Tokens (k)USD -
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base49.11%376.80.61
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demo70.90%340.80.55
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reflect67.94%331.90.56
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- Panofy · Opus 4.6Panofy · Opus 4.6 - high · avg@3 over 12 task groupshigh · 12 个 task group 的 avg@3 -
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- Mode模式 - avg@3Tokens (k)Tokens (k)USD -
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base50.17%318.90.72
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demo68.24%383.50.80
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reflect67.98%365.20.79
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- Held-out accuracy by task group (avg@3, %)各 task group 的 held-out 准确率 (avg@3, %) - - base - demo - reflect - -
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- avg@3 = mean held-out accuracy over 3 attempts per task. The task-group chart below keeps Codex, Claude Code, and Panofy side by side under the same base, demo, and reflect settings. - avg@3 = 每个任务 3 次尝试的 held-out 准确率均值。下面的 task-group 图并排展示 Codex、Claude Code 与 Panofy 在 base、demo、reflect 下的结果。 -

-

- Board summaries: +20.31 pp / −8.69% cost (Claude Code) · +18.21 pp / −25.75% cost (Codex) · +17.94 pp / +11.82% cost (Panofy). - Board 汇总:+20.31 pp / −8.69% cost（Claude Code）· +18.21 pp / −25.75% cost（Codex）· +17.94 pp / +11.82% cost（Panofy）。 -

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- Full per-task reports under experiments/codex_gpt5_5_xhigh/, experiments/claude_code_opus_4_8_xhigh/, and experiments/panofy_claude_opus_4_6_high/. - 完整逐任务报告见 experiments/codex_gpt5_5_xhigh/、experiments/claude_code_opus_4_8_xhigh/ 与 experiments/panofy_claude_opus_4_6_high/。 -

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- Why do skills cost less? Read the blog →skill 为什么反而更省？阅读博客 → -

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- Real company interfaces — CRM, ERP, Finance. - 真实的公司业务接口 —— CRM、ERP、Finance。 -

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- Each group is one shared business world with lookalike records mixed in, seeded from real-job - sources such as GDPVal and - SOP-Bench. - 每组都是一个共享的业务世界,混入了大量"长得很像"的干扰记录;场景来自像 - GDPVal 和 - SOP-Bench 这样的真实工作任务源。 -

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- Group编号 - Domain领域 - Business focus业务场景 -
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01CRMMarketing lead capture市场营销线索捕获
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02CRMB2B quote & account responseB2B 报价与客户响应
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03CRMService ticket resolution服务工单处理
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04CRMRetention & churn analytics留存与流失分析
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05ERPFinance expense control & close费用控制与账务结算
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06ERPProcurement, supplier & receiving采购、供应商与收货
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07ERPInventory & order fulfillment库存与订单履约
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08FinancePersonal advisory, tax & estate个人理财、税务与遗产规划
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09FinanceOperational modeling & reporting运营建模与管理报表
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10FinanceInvestment strategy & portfolio risk投资策略与组合风险
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11FinanceCredit risk & lending committee信用风险与信贷委员会
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12ERPHR employee lifecycle & policyHR 员工生命周期与制度
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- How the hidden rules are planted, how the data is built and graded by agents, and why cost goes - down — read the blog → - 隐藏规则怎么埋、数据怎么由 agent 构造与评分、成本为什么会下降 —— - 阅读博客 → -

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GDPevo
-

Measuring agent self-evolution on GDP-worthy work.在真实业务工作上衡量 Agent 的自我进化。

- github.com/Prism-Shadow/GDPevo -
-
- - +
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b/site/scripts/postbuild.mjs @@ -0,0 +1,9 @@ +import { cp, copyFile } from "node:fs/promises"; +import { dirname, join } from "node:path"; +import { fileURLToPath } from "node:url"; + +const root = dirname(dirname(fileURLToPath(import.meta.url))); +const dist = join(root, "dist"); + +await copyFile(join(root, ".nojekyll"), join(dist, ".nojekyll")); +await cp(join(root, "assets"), join(dist, "assets"), { recursive: true }); diff --git a/site/src/App.jsx b/site/src/App.jsx new file mode 100644 index 00000000..32ad1c13 --- /dev/null +++ b/site/src/App.jsx @@ -0,0 +1,65 @@ +import { useEffect, useState } from "react"; +import { Header, Footer } from "./components/Layout.jsx"; +import { homeContent } from "./content/home.js"; +import { BlogPage } from "./pages/BlogPage.jsx"; +import { HomePage } from "./pages/HomePage.jsx"; +import { initialLang, localize } from "./lib/i18n.jsx"; +import { initialThemeChoice, resolveTheme } from "./lib/theme.js"; + +function currentPage() { + return window.location.pathname.endsWith("/blog.html") ? "blog" : "home"; +} + +export default function App() { + const [lang, setLang] = useState(initialLang); + const [themeChoice, setThemeChoice] = useState(initialThemeChoice); + const page = currentPage(); + + useEffect(() => { + if (!window.location.hash) return; + requestAnimationFrame(() => { + const id = window.location.hash.slice(1); + document.getElementById(id)?.scrollIntoView(); + }); + }, [page]); + + useEffect(() => { + document.body.classList.toggle("blog", page === "blog"); + document.title = localize(homeContent.documentTitle[page], lang); + }, [page, lang]); + + useEffect(() => { + document.documentElement.setAttribute("data-lang", lang); + document.documentElement.setAttribute("lang", lang === "zh" ? "zh-Hans" : "en"); + try { + localStorage.setItem("gdpevo-lang", lang); + } catch (e) { + // Ignore storage access errors in restricted browser contexts. + } + }, [lang]); + + useEffect(() => { + const apply = () => { + document.documentElement.setAttribute("data-theme", resolveTheme(themeChoice)); + document.documentElement.setAttribute("data-theme-choice", themeChoice); + }; + apply(); + try { + localStorage.setItem("gdpevo-theme", themeChoice); + } catch (e) { + // Ignore storage access errors in restricted browser contexts. + } + const mq = window.matchMedia && window.matchMedia("(prefers-color-scheme: dark)"); + if (!mq || themeChoice !== "system") return undefined; + mq.addEventListener("change", apply); + return () => mq.removeEventListener("change", apply); + }, [themeChoice]); + + return ( + <> +
+ {page === "blog" ? : } +