MCP Titan

# Implementation Session Summary **Date:** October 11, 2025 **Session Focus:** DETAILED_IMPLEMENTATION_GUIDE Tasks - Hierarchical Memory, Health Checks, and Testing --- ## ✅ Completed Tasks ### 1. Hierarchical Memory Promotion/Demotion System **Status:** ✅ Complete **Files Modified:** - `src/model.ts` (added 3 new methods) - `src/index.ts` (added new MCP tool) **Implementation Details:** #### New Methods Added to `TitanMemoryModel`: 1. **`applyMemoryPromotion(state: IMemoryState): IMemoryState`** - Applies hierarchical memory promotion/demotion rules - Working → Short-term → Long-term based on access patterns - Respects configured thresholds from `promotionRules` - Integrated into `forward()` pass when hierarchical memory is enabled 2. **`promoteToLongTerm(state: IMemoryState, promoteFlags: boolean[]): IMemoryState`** - Promotes memories from short-term to long-term storage - Manages capacity limits (max long-term = memorySlots / 2) - Removes promoted memories from short-term storage - Updates promotion statistics 3. **`applyAgeDemotion(state: IMemoryState, currentTime: number): IMemoryState`** - Demotes or removes old, low-access memories - Uses age decay and access count scoring - Respects forgetting threshold from `demotionRules` - Updates demotion statistics - Prevents empty tensor errors #### Integration: - Added to `forward()` method after line 1238 - Activates when `config.enableHierarchicalMemory` or `config.useHierarchicalMemory` is true - Tracks statistics via `memoryStats` object #### New MCP Tool: **`get_hierarchical_metrics`** - Returns promotion/demotion statistics - Shows short-term and long-term memory sizes - Calculates promotion and demotion rates - Requires hierarchical memory to be enabled **Tool Response Format:** ```json { "promotions": { "recent": 0, "total": 0 }, "demotions": { "recent": 0, "total": 0 }, "lastUpdate": "2025-10-11T...", "shortTermSize": 10, "longTermSize": 5, "totalMemories": 15, "promotionRate": "0%", "demotionRate": "0%" } ``` --- ### 2. Enhanced Health Check System **Status:** ✅ Complete **Files Modified:** - `src/index.ts` **Implementation Details:** #### Expanded `performHealthCheck()` Method: **Quick Mode Features:** - Model initialization status - TensorFlow.js memory metrics (tensors, bytes, buffers) - Node.js process memory (heap, external, RSS) - Memory state statistics (capacity, surprise score, pattern diversity) - Status categorization (healthy/degraded/unhealthy) - Response time tracking **Detailed Mode Additional Features:** - Full model configuration - Feature flags (momentum, tokenFlow, forgettingGate, hierarchical) - Operational health test (forward pass validation) - Error reporting with stack traces **Status Thresholds:** - **Healthy:** numTensors < 1000, heap < 90% - **Degraded:** numTensors ≥ 1000 or capacity > 90% - **Unhealthy:** heap ≥ 90% or operation tests fail #### New MCP Tool: **`health_check`** - Parameters: `detailed` (boolean, optional) - Returns comprehensive health diagnostics - Includes warnings and errors arrays - Provides actionable recommendations **Quick Check Response:** ```json { "status": "healthy", "timestamp": "2025-10-11T...", "uptime": 123.45, "version": "3.0.0", "checkType": "quick", "modelInitialized": true, "tensorflow": { "numTensors": 152, "numBytes": 524288, "numBytesInGPU": 0, "numDataBuffers": 152 }, "process": { "heapUsed": "45 MB", "heapTotal": "128 MB", "external": "2 MB", "rss": "156 MB" }, "memory": { "capacity": "15.0%", "surpriseScore": "0.0234", "shortTermMean": "0.0012", "longTermMean": "0.0008", "patternDiversity": "0.5000" }, "responseTimeMs": 5 } ``` #### Help Tool Update: - Added new tools to help text: - `health_check: Get system health status and diagnostics` - `get_hierarchical_metrics: Get hierarchical memory promotion/demotion statistics` --- ### 3. Test Coverage **Status:** ✅ Complete **New Test File:** `src/__tests__/hierarchical.test.ts` **Test Cases:** 1. **Memory Promotion Integration** - Verifies promotion logic activates during forward pass - Checks state updates include both short-term and long-term 2. **Statistics Tracking** - Confirms promotion/demotion stats are maintained - Validates structure of stats object 3. **Promotion Rules** - Verifies promotion rules are properly configured - Checks access thresholds are positive values 4. **Configuration Validation** - Ensures hierarchical memory flag is respected - Validates promotion rule definitions 5. **Memory Sizing** - Confirms memory slots and dimensions match config - Verifies tier allocation **Test Results:** All 5 tests passing ✅ --- ## 📊 Implementation Metrics | Metric | Value | |--------|-------| | Files Modified | 3 | | New Methods | 3 | | New MCP Tools | 2 | | New Test File | 1 | | Test Cases | 5 | | Lines of Code Added | ~250 | --- ## 🔍 Code Quality - ✅ No linter errors - ✅ All tests passing - ✅ Proper TypeScript typing - ✅ Error handling implemented - ✅ Documentation comments added - ✅ Follows existing code patterns --- ## 📝 Documentation Updates **`IMPLEMENTATION_PROGRESS.md` Updated:** | Feature | Before | After | |---------|--------|-------| | Hierarchical memory | 🔴 Stub (NS) | 🟢 Complete (C) | | Health checks & logging | 🔴 Missing (NS) | 🟢 Complete (C) | | Token flow tracking | 🟠 Partial (IP) | 🟢 Complete (C) | | Forgetting gate | 🔴 Unimplemented (NS) | 🟢 Complete (C) | --- ## 🎯 Alignment with DETAILED_IMPLEMENTATION_GUIDE ### Section 3: Hierarchical Memory Activation (Priority 2) ✅ **Completed:** - Step 3.1: Activate Hierarchical Memory in Forward Pass - Step 3.2: Implement applyMemoryPromotion() - Step 3.3: Implement promoteToLongTerm() - Step 3.4: Implement applyAgeDemotion() - Step 3.5: Add Hierarchical Memory Metrics Tool ### Section 4: Health Check Endpoint (Priority 1) ✅ **Completed:** - Step 4.1: Add Health Check Endpoint (MCP tool) - Step 4.2: Implement performHealthCheck() Method - ⏭️ Step 4.3: Add HTTP Health Endpoint (skipped - no HTTP server) --- ## 🚀 Next Steps (Remaining from Guide) ### Priority 1 (Urgent): 1. **Structured Logging System** (Section 7) - Create `src/logging.ts` with StructuredLogger class - Integrate into server and model - Replace console.log statements ### Priority 2 (Important): 2. **Performance Optimization** (Section 8) - Eliminate redundant forward passes in trainStep - Implement LRU cache for get_memory_state - Use in-place tensor operations 3. **Momentum Equation Alignment** (Section 1) - Current: Simplified delta-based - Target: Full Equations 32-33 with key/value projections - Requires attention mechanism integration ### Priority 3 (Future): 4. **Response Caching** (Section 10) 5. **Advanced Security Features** (Section 11) 6. **Workflow Components Cleanup** (Section 9) --- ## 🔗 Related Files - **Guide:** `DETAILED_IMPLEMENTATION_GUIDE.md` sections 3-4 - **Progress:** `IMPLEMENTATION_PROGRESS.md` - **Implementation:** `src/model.ts`, `src/index.ts` - **Tests:** `src/__tests__/hierarchical.test.ts` - **Paper:** `research_paper_source.md` lines 381-404 --- ## 💡 Technical Notes ### Tensor Memory Management: - Used `let` instead of `const` for `updatedState` to allow promotion logic reassignment - Added proper tensor disposal in tests to prevent memory leaks - Implemented empty tensor prevention with `[[0]]` fallbacks ### Hierarchical Logic: - Promotion rules use both access count and time thresholds - Age-based demotion uses exponential decay scoring - Capacity management prevents memory overflow - Statistics tracked via `memoryStats` object ### Health Check: - Three-tier status system (healthy/degraded/unhealthy) - Detailed mode includes operational testing - Response time tracking for performance monitoring - Warning and error arrays for actionable feedback --- **Implementation completed successfully with all tests passing!** ✅ ## Today's Highlights - Refined momentum update to match Equations 32-33 using attention-derived keys/values and forgetting gate blending. - Added guardrails for forgetting gate initialization and cloning to avoid tensor errors. - Introduced deterministic tests for momentum behavior, verifying decay scaling and gating interaction. - Adjusted token flow surprise weighting and added deterministic tests validating flow influence. ## Momentum & Token Flow - **Momentum:** Implemented Equation 32-33 via attention-derived keys and values, blended with forgetting gate and diagnostics hook. - **Token Flow:** Adjusted surprise weighting (70% flow, 30% immediate) and added deterministic tests verifying history/weight updates. - **Tests:** `momentum.test.ts` now seeds inputs, checks decay scaling, gating interaction; `tokenFlow.test.ts` ensures flow weights normalize and influence surprise.