Evolution Log — Motion Autocomplete: AI predicts your next physical movement

**Goal:** Evolve Gen 6 heuristic prototype into a runnable multimodal prediction stack with testable orchestration behavior. ### Delivered - Multimodal schemas (`MotionFrame`) combining IMU, pose, and local context. - Fusion predictor with sliding-window feature aggregation and intent scoring. - Intent set expanded to: `lifting_object`, `turning_around`, `reaching_for_object`, `walking_towards_exit`, `idle`. - Context orchestrator upgraded to produce structured action plans with confidence gating and cooldown suppression. - Deterministic stream simulator for multiple scenarios. - CLI enhancements: scenario selection, `all` mode, JSON output. - Runtime config added in `config/default.json`. - Tests: predictor unit tests, orchestrator unit tests, CLI smoke test. - Packaging/runtime scaffolding: `pyproject.toml`, `Makefile`, `Dockerfile`. ### Findings - Feature-level fusion improves interpretability versus single-axis heuristics. - Cooldown suppression is required to avoid noisy repetitive automation. - Deterministic simulation seeds make regression testing stable across generations. ### Next Suggestion (Generation 8) Replace heuristic scoring with a lightweight learned temporal model and attach real endpoint adapters (Graph Kernel / Perception Mesh) to evaluate latency under live input. **Quality Score:** 0.9 **Files Changed:** `ARCHITECTURE.md`, `EVOLUTION.md`, `.gitignore`, `src/models/motion.py`, `src/engine/predictor.py`, `src/context/orchestrator.py`, `src/main.py` **Commits:** `dc2b8f2` **Artifacts:** Technical Specification, Motion Prediction Engine (Heuristic), Context Orchestrator, Real-time Simulation Tool **Next Suggestion:** Replace heuristics with lightweight sequence model and add multimodal sensor fusion.