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| # TGI / TLM System Audit (March 2026) | |
| ## 1. Documentation Gaps (Genuine Gaps) | |
| - **API Completeness**: 556 classes/methods lack docstrings in `docs/API.md`. High-impact modules like `algebraic.py` and `aimo_3_gateway.py` are largely undocumented. | |
| - **Dependency Map**: No `requirements.txt` or `pyproject.toml` exists, making environment setup brittle (identified during the audit). | |
| - **Architecture Visualization**: No diagram or formal specification for the Five-Core interaction, though `tgi_system_demo.py` provides a functional example. | |
| ## 2. Structural Weaknesses | |
| - **Unit Testing**: Major `research/` modules (`tlm.py`, `knowledge_mapper.py`, `agentic_bridge.py`) lack unit tests. The system relies heavily on high-level demos and integration tests. | |
| - **Error Handling**: `ActionMapper` and `AgenticBridge` use deterministic mappings without robust fallbacks or error-correction for "out-of-manifold" inputs. | |
| - **Agentic Autonomy**: While the "Action Engine" can generate plans, the actual execution (looping back result to TGI, handling retries) is not implemented. | |
| - **Mobile Packaging**: The `android/` directory contains skeletons, but no automated build or CI/CD for the APK exists yet. | |
| ## 3. High-Impact Improvements | |
| - **TLM-Based Intent Lifting**: Replace MD5 hashing in `ActionMapper.resolve_intent` with a grounded TLM lifting to ensure semantic alignment. | |
| - **Hardware-Responsive Manifolds**: Implement the dynamic $m$ resizing based on `HardwareMapper` telemetry as proposed in Phase 7. | |
| - **Closed-Loop Autonomy**: Implement the "Agentic Action Engine" executor that can actually call MCP tools and return results to the TGI core. | |
| - **Symbolic Solver Expansion**: Integrate the `AIMOReasoningEngine` more deeply into the `TGIAgent.query` path for complex multi-step math problems. | |
| ## 4. Prioritized Next Phase (Phase 8: Closed-Loop Sovereign Autonomy) | |
| 1. **Infrastructure**: Create `requirements.txt` and a basic unit test suite for core `research/` modules. | |
| 2. **Refinement**: Implement full TLM-based intent lifting in `ActionMapper`. | |
| 3. **Execution**: Build the `ActionExecutor` to bridge the gap between "Plan Generation" and "Autonomous Task Completion". | |
| 4. **Resilience**: Add Law-based guardrails to the `AgenticBridge` to prevent "Topological Drift" during multi-step plans. | |
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| *Audit performed by Jules — March 2026* | |