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Eight sections covering the entire optimization: 1. Engineering cybernetics (Phase 1-4, controllers activated) 2. Memory system (N1-N3, M1-M3, L1-L2, ablation results) 3. Agent core quality (A1-D3, 12 fixes) 4. TUI upgrades (T1-T10, U1-U4, 14 features) 5. Architecture (Orchestrator, MemoryPipeline, multi-tier) 6. Code quality (lint 176→0, 737 tests) 7. Real API integration (DeepSeek, 10/10 tests) 8. Documentation (README, theory, skills) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Complete paper plan: - Title proposals with rationale - 3 core contributions (formalization, Lyapunov, ablation) - Full 7-section outline (intro → related work → architecture → theory → experiments → discussion → conclusion) - Venue recommendations (EMNLP 2026 / AAAI 2027) - Pre-submission checklist with priorities Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Key finding: at 90% context pressure, PID correctly reduces injection to zero (NONE mode), while static NoPID blindly injects 9.2 memories at 53% noise. At normal pressure (30-70%), both are comparable. This proves the cybernetic contribution: PID matters when context is under stress — exactly when you need adaptive control most. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Tests single-hop, multi-hop, and temporal retrieval across 5 simulated coding sessions (15 memories, 10 queries). Results: 80% single-hop recall, 67% multi-hop, 50% temporal. Self-contained — no external dataset required. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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