Module llm Future

LLM Module - Future Enhancements

Scope

This document covers forward-looking hardening and capability expansion for the LLM module beyond currently shipped behavior in core runtime paths.

Synchronization Evidence (2026-07-19)

• Canonical content pass completed against module source code
• All priorities extracted and cross-validated with ROADMAP.md
• Doxygen documentation extended to 100% of .cpp source files
• Build/test evidence collected from module_llm_test_active_vram_allocator_focused

Design Constraints

• Preserve stable inference and streaming interfaces for existing callers.
• Avoid implicit policy bypass paths when introducing new routing/execution shortcuts.
• Keep model/adapter lifecycle ownership explicit across manager and plugin layers.
• Keep optional backend features degradable with clear fallback behavior.

Required Interfaces

Interface	Consumer	Notes
Engine submit and streaming APIs	server handlers, adapters, orchestration layers	Must stay backward-compatible across minor releases
Router and shard-load update interfaces	scheduler, route selection, distributed execution	Must provide deterministic fallback when telemetry is missing
Plugin and adapter lifecycle APIs	plugin manager, deployment/orchestration paths	Must expose explicit load/unload/error states
Policy and prompt guard interfaces	API handlers and inference submit paths	Must execute before backend inference calls

Implementation Notes

• Complete remaining distributed speculative-routing and fan-out hardening.
• Consolidate runtime metrics and error-surface semantics across async/enhanced engines.
• Expand adapter and cache isolation behavior for high-concurrency tenant scenarios.
• Align operator observability surfaces with real runtime controls and failure states.

Wave B B3: Multi-Task LoRA Fine-Tuning

Priority: High Target: Q1–Q2 2027

• design shared LoRA base with task-specific projection heads
• add domain-gating to route requests to task-specialized adapters
• add joint multi-task loss with configurable weighting
• add ablation workflow to compare shared vs separate adapters
• run three-task benchmark evaluation for transfer and robustness

Test Strategy

• Add focused tests for distributed execution fallback and partial-failure merge behavior.
• Add stress tests for queue saturation, cancellation storms, and adapter churn.
• Add regression suites for policy enforcement and prompt safety checks under load.
• Keep benchmark-to-target mappings source-verifiable and release-repeatable.

Performance Targets

• Maintain stable token throughput and prompt latency against current release baselines.
• Keep routing and orchestration overhead bounded under concurrent multi-shard requests.
• Keep cache and adapter operations within bounded latency envelopes under contention.

Security / Reliability

• Preserve deny-by-default behavior for invalid or unauthorized inference requests.
• Ensure adapter/model path handling remains trust-bound and canonicalized.
• Ensure audit and diagnostics contain enough context for incident triage.
• Require explicit fallback signaling on partial distributed failures.

Wave B Acceptance Gates (B3)

• average task performance ≥ +8% vs single-task baseline
• training-time increase ≤ 15%
• robust behavior across task configurations

Related Documents

• AI wave tracker: ../ai/ROADMAP.md
• bibliography: ../../docs/research/ml_enhancements_bibliography.md
• issue scope: https://github.com/makr-code/ThemisDB/issues/5039

Risk Backlog

• Distributed optimization regressions under heterogeneous backend availability.
• Cache consistency edge cases during rapid model/adapter lifecycle operations.
• Operational drift between documented and deployed backend feature combinations.

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