# ๐Ÿ—๏ธ OpenSkyNet Architecture: The 5 Jewels Integration ``` โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ HEARTBEAT LOOP (~1 Hz) โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ SPARSE METABOLISM โ”‚ โ† Decides WHAT to compute โ”‚ โ”‚ โ”‚ (5% of cycle) โ”‚ Based on frustration โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ READ KERNEL STATE + JEPA โ”‚ โ”‚ โ”‚ โ”‚ - successRate, failureCount โ”‚ โ”‚ โ”‚ โ”‚ - frustration (0-1) โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ INNER DRIVES โ”‚ โ† EXISTING (unchanged) โ”‚ โ”‚ โ”‚ - curiosity, exploration, etc โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ (only if metabolism says so) โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ NEURAL LOGIC ENGINE โ”‚ โ† NEW Jewel #1 โ”‚ โ”‚ โ”‚ 64 learned rules in latent space โ”‚ SIN LLM โ”‚ โ”‚ โ”‚ - Infer state based on patterns โ”‚ 10-15ms โ”‚ โ”‚ โ”‚ - Return confidence โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ (only if metabolism says so) โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ HIERARCHICAL MEMORY โ”‚ โ† NEW Jewel #2 โ”‚ โ”‚ โ”‚ โ”Œโ”€ Level 0: Working Memory (7 items) โ”‚ 4 LEVELS โ”‚ โ”‚ โ”‚ โ”œโ”€ Level 1: Episodic (tensor states) โ”‚ 30-40ms โ”‚ โ”‚ โ”‚ โ”œโ”€ Level 2: Semantic (concepts) โ†โ”€โ”€โ”€โ”€โ”ค Consolidation โ”‚ โ”‚ โ”‚ โ””โ”€ Level 3: Procedural (skills) โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ JEPA DRIVE ENHANCEMENT (+ Logic) โ”‚ โ† Plan B (existing) โ”‚ โ”‚ โ”‚ frustration > 0.5 โ†’ boost drive โ”‚ + NLE input โ”‚ โ”‚ โ”‚ + Lyapunov damping (next step) โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ (only if metabolism says so) โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ LYAPUNOV CONTROLLER โ”‚ โ† NEW Jewel #3 โ”‚ โ”‚ โ”‚ Compute divergence โ†’ damping factor โ”‚ Homeostasis โ”‚ โ”‚ โ”‚ Prevents "thermal epilepsy" (V7 bug) โ”‚ 5-8ms โ”‚ โ”‚ โ”‚ Apply damping to drive.urgency โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ (only if complexity high) โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ CAUSAL REASONER โ”‚ โ† NEW Jewel #4 โ”‚ โ”‚ โ”‚ Build DAG of cause-effect โ”‚ Causal not correl โ”‚ โ”‚ โ”‚ Reason about interventions โ”‚ 10-20ms โ”‚ โ”‚ โ”‚ Detect confounders โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ AUTONOMY LOGGER โ”‚ โ† Plan B (existing) โ”‚ โ”‚ โ”‚ Log decision + all context โ”‚ + Extended fields โ”‚ โ”‚ โ”‚ nleConfidence, hmSize, lyapunovDamping โ”‚ <1ms โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ EXECUTE DECISION โ”‚ โ”‚ โ”‚ โ”‚ If drive.kind != 'idle' โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ†“ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ OPTIONAL: BIFรSIC ODE INTEGRATION โ”‚ โ† Phase 5 (conditional) โ”‚ โ”‚ โ”‚ If autonomy >= 95%, add spike generationโ”‚ Decision trigger โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ†“ LOOP ~1Hz ``` --- ## ๐Ÿ”„ Memory Consolidation (Sleep-like Process) ``` โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ OVER TIME (Idle Periods) โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ โ”‚ โ”‚ Heartbeat #1 Heartbeat #2 Heartbeat #3 โ”‚ โ”‚ โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ โ”‚ โ”‚ โ”‚ โ”‚ Working Memory: Working Memory: Working Memory: โ”‚ โ”‚ [ drive_A ] [ drive_B ] [ drive_C ] โ”‚ โ”‚ โ†“ (3+ episodes similar) โ”‚ โ”‚ โ†“ โ”‚ โ”‚ Episodic: Episodic: โ”‚ โ”‚ [zโ‚] [zโ‚, zโ‚‚, zโ‚ƒ] Consolidate โ†’ โ”‚ โ”‚ โ”‚ โ”‚ Semantic Memory: โ”‚ โ”‚ "Pattern: high-frustrationโ”‚ โ”‚ โ†’ exploration" โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ ``` --- ## ๐Ÿ“Š Metabolism Levels by Frustration ``` Frustration Total Active Components 0.0 โ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [NLE, Logger] 0.3 โ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [NLE, HM, JEPA, Logger] 0.5 โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘ [NLE, HM, JEPA, Lyapunov, Logger] 0.7 โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘ [ALL 5 fully active] 1.0 โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ [FULL EMERGENCY MODE] ``` --- ## ๐ŸŽ“ Why Each Jewel Matters ### 1๏ธโƒฃ Neural Logic Engine - **Problem:** OpenSkyNet asks LLM "what next?" โ†’ slow, depends on API - **Solution:** Learn 64 pattern-rules in latent space, infer instantly - **Result:** <10ms per decision, zero LLM calls ### 2๏ธโƒฃ Hierarchical Memory - **Problem:** Logs are write-only, no learning from past - **Solution:** 4-level memory + automatic consolidation - **Result:** Can retrieve similar past situations, find patterns ### 3๏ธโƒฃ Lyapunov Controller - **Problem:** V7_METABOLISM "epileptic" when frustrated - **Solution:** Monitor divergence, apply dynamic damping - **Result:** Stays stable under pressure, doesn't explode ### 4๏ธโƒฃ Causal Reasoner - **Problem:** LLMs see "A happened before B" โ†’ assume A caused B - **Solution:** Build DAG of true causal chains, detect confounders - **Result:** Interventions are smarter (avoid backfires) ### 5๏ธโƒฃ Sparse Metabolism - **Problem:** Run all 5 components every cycle โ†’ inefficient - **Solution:** Activate only what's needed based on frustration - **Result:** Stays fast, scales to 10Hz+ if needed --- ## ๐Ÿšฆ Decision Flow Example ### Scenario: "Frustration spike to 0.8, need to act" ``` 1. SPARSE METABOLISM frustration=0.8 โ†’ Activate ALL 5 components metabolic_rate = 95% 2. NEURAL LOGIC ENGINE State: [frustration=0.8, success_rate=0.2, ...] Active rules: [curiosity_boost, exploration_trigger] Confidence: 0.87 3. HIERARCHICAL MEMORY Query episodic: Found 3 similar states from yesterday All led to "exploration" โ†’ semantic pattern confirms Suggest: "Try alternatives" 4. JEPA DRIVE ENHANCEMENT Base drive: curiosity@0.4 JEPA boost: +0.3 (frustration > 0.5) New drive: curiosity@0.7 5. LYAPUNOV CONTROLLER Divergence: 0.38 (high but rising) Damping: 0.4 (moderate brake) Final drive: curiosity@0.42 (dampened from 0.7) 6. CAUSAL REASONER Action "explore_alternatives" โ†’ causes? Direct: [success_rate โ†‘, entropy โ†‘] Indirect: [frustration โ†“ (if success)] Confounders: "previous_failures" may backfire Confidence: 0.65 7. EXECUTE โ†’ Execute: curiosity@0.42 with monitoring 8. LOG Recorded: [nleConfidence=0.87, hmMatches=3, lyapunovDamping=0.4, causalConfidence=0.65 metabolicRate=0.95] 9. NEXT CYCLE See result โ†’ update episodic memory If success โ†’ consolidate to semantic If backfire โ†’ Causal Reasoner learns confounder ``` --- ## ๐ŸŽฏ Performance Targets | Metric | Before | After | Unit | |--------|--------|-------|------| | Autonomy | 90% | 99% | % | | LLM calls | 80% | <5% | % of decisions | | Heartbeat latency | 50ms | <100ms | ms | | Memory levels | 1 | 4 | count | | Causal nodes learned | 0 | 10-50 | count | | Metabolic overhead | 100% | 30-95% | % adaptive | | Divergence max | 0.5 | <0.35 | exponent | --- ## ๐Ÿ“ File Structure ``` src/omega/ โ”œโ”€โ”€ heartbeat.ts (MODIFIED - new flow) โ”œโ”€โ”€ neural-logic-engine.ts (NEW - 350 lines) โ”œโ”€โ”€ hierarchical-memory.ts (NEW - 380 lines) โ”œโ”€โ”€ lyapunov-controller.ts (NEW - 300 lines) โ”œโ”€โ”€ causal-reasoner.ts (NEW - 280 lines) โ”œโ”€โ”€ sparse-metabolism.ts (NEW - 320 lines) โ”œโ”€โ”€ jepa-drive-enhancement.ts (EXISTING - optimized) โ””โ”€โ”€ autonomy-logger.ts (EXISTING - extended) docs/ โ””โ”€โ”€ UPGRADE_PLAN_PHASE4.md (NEW - integration guide) ``` --- ## โœ… Readiness Checklist - โœ… Neural Logic Engine (implemented) - โœ… Hierarchical Memory (implemented) - โœ… Lyapunov Controller (implemented) - โœ… Causal Reasoner (implemented) - โœ… Sparse Metabolism (implemented) - โณ Integrate into heartbeat.ts - โณ Test each component individually - โณ Test integration - โณ Validate autonomy >= 99% - โณ Measure latency - โณ Monitoring dashboard (extend live-autonomy-monitor.mjs) --- ## ๐Ÿ”ฎ Future Directions ### Phase 5 (Optional, if autonomy > 95%) - Integrate Bifรกsic ODE for spike-based decisions - Add "dreaming" mode (offline replay + consolidation) ### Phase 6 (Optional, if needed) - Attention mechanism between memory levels - Cross-memory retrieval (episodic โ†’ semantic โ†’ procedural) - Skill learning (procedural memory update) ### Phase 7 (Research) - Multi-scale reasoning (microsecond to hours) - Modal reasoning (different reasoning modes per domain) - Symbolic-neural integration (ASP + neural jointly)