MCP Titan

# MCP-Titan System Audit & Modernization Plan ## Research Paper Alignment Gap Analysis ### Critical Missing: Titans Core Concepts The research paper describes advanced Long-term Memory Module (LMM) concepts that are partially or not implemented: 1. **Momentum-Based Memory Updates** (Equations 32-33) - Paper: `M_t = diag(1-α_t)M_t + S_t` with momentum term `S_t = diag(η_t)S_{t-1} - ...` - Current: `src/model.ts` lines 1500-1600 implements basic gradient updates but lacks momentum tracking - Gap: No momentum state (S_t) or eta parameter tracking in IMemoryState 2. **Token Flow Integration** - Paper (Section 3.1): Memory updates should consider both "momentary surprise" and "token flow" - Current: Surprise tracking exists (`surpriseHistory` in IMemoryState) but no explicit token flow modeling - Gap: Missing sequential token dependency tracking in memory updates 3. **Deep Neural Memory Module** - Paper: Uses neural networks for memory storage instead of simple matrices - Current: `memoryProjector` exists but memory itself stored as tensors, not as trainable neural module - Gap: Memory is tensor-based not neural-network-based as paper suggests 4. **Forgetting Gate with Weight Decay** - Paper: Forgetting gate `α_t` equivalent to weight decay, with parallel training optimization - Current: Basic memory pruning exists (`src/pruning.ts`) but not integrated with gradient-based forgetting - Gap: No learnable forgetting gate parameter in forward/train steps ## Phase 1: Documentation Drift & Accuracy Issues ### 1.1 Critical Documentation Misalignments **docs/api/README.md** (lines 39-98) - Claims tool `process_input` exists - NOT FOUND in `src/index.ts` tool registry - Actual tools: `help`, `init_model`, `forward_pass`, `train_step`, `get_memory_state`, `manifold_step`, `prune_memory`, `save_checkpoint`, `load_checkpoint`, `reset_gradients` - Missing: Documentation for `bootstrap_memory`, `encode_text`, `get_surprise_metrics`, `analyze_memory`, `predict_next` **ROADMAP_ANALYSIS.md** (line 17) - States "16 sophisticated memory tools" but only 10 tools registered in `src/index.ts` lines 142-882 **IMPLEMENTATION_COMPLETE.md** - Claims "production-ready" and "complete training pipeline" but: - `PRODUCTION_READINESS_ANALYSIS.md` explicitly states "NOT production-ready" - Contradictory documentation needs reconciliation **docs/llm-system-prompt.md** - Contains cursor rules but may not reflect actual tool schemas - Needs validation against `src/index.ts` Zod schemas ### 1.2 Outdated Architecture Documentation **docs/architecture-overview.md** - No mention of `src/workflows/` directory (5 workflow files) - Missing: GitHubWorkflowManager, LintingManager, WorkflowOrchestrator components - No documentation of VectorProcessor, SafeTensorOps, MemoryPruner classes **README.md** - Installation instructions reference `mcp-titan` command but `package.json` defines `titan-memory` as bin - Version mismatch: README may not reflect v3.0.0 ## Phase 2: Implementation Completeness Audit ### 2.1 Core Model Implementation Gaps **src/model.ts (TitanMemoryModel)** Line 230-3240: Extensive implementation but missing key research concepts: 1. **Momentum State Not Persisted** - `trainStep()` uses optimizer momentum but doesn't expose S_t for memory updates - Fix needed in lines 1800-2000 where training happens 2. **Hierarchical Memory Half-Implemented** - Lines 251-276: `extendedMemoryState` and `promotionRules` defined - But: No usage found in `forward()` or `trainStep()` methods - Methods `updateMetaMemory()`, `manifoldStep()` marked unimplemented (line 2890+) 3. **Quantization Stub Only** - Lines 570-650: Quantization methods exist but always disabled - `config.enableQuantization` never set to true anywhere 4. **Contrastive Learning Incomplete** - Lines 700-800: Contrastive loss computation exists - But: No negative example buffer management in actual training loop ### 2.2 Tool Implementation Issues **src/index.ts (TitanMemoryServer)** Line 140-882: Tool registration analysis: 1. **Inconsistent Error Handling** - Some tools use try-catch with telemetry (lines 400-500) - Others directly throw errors (lines 600-700) - Standardization needed 2. **Missing Parameter Validation** - `forward_pass` accepts both string and array but no type guard before processing - `train_step` doesn't validate x_t and x_next have same dimensions 3. **Memory State Synchronization Issues** - `memoryState` updated in tools but not automatically persisted - `autoSaveInterval` set (line 62) but save logic in lines 1100-1200 may fail silently ### 2.3 Tokenizer & Embedding Gaps **src/tokenizer/** directory 1. **BPE Training Not Integrated** - `bpe.ts` has training methods but never called from main server - No automatic vocabulary building on first run 2. **Embedding Dimension Mismatches** - Default inputDim: 768 (line 82 of index.ts) - But embedding layer may use different dimension if loaded from checkpoint - No validation in `loadCheckpoint` tool ## Phase 3: Code Quality & Technical Debt ### 3.1 TypeScript Issues **Compilation Warnings Present:** - `src/model.ts` line 3: `/* eslint-disable @typescript-eslint/no-unused-vars */` - indicates unused imports - `src/utils/polyfills.ts` has recent modifications (git status) - may have breaking changes - Type safety concerns: `tokenizer?: any` (src/index.ts line 60) should be properly typed ### 3.2 Testing Coverage Gaps **src/tests/** analysis: 1. **No Integration Tests** - Individual unit tests for model, learner, pruning, tokenizer - Missing: End-to-end MCP tool invocation tests - Missing: Multi-step workflow tests (init → train → save → load → predict) 2. **Mock-Heavy Tests** - `learner.test.ts` uses MockModel and MockTokenizer - Real component interaction testing needed 3. **Edge Case Coverage** - No tests for malformed MCP requests - No tests for concurrent tool calls - No tests for memory overflow scenarios ### 3.3 Performance & Scalability Issues **Identified Bottlenecks:** 1. **Memory Leaks Risk** - `wrapWithMemoryManagement` in src/index.ts (lines 91-99) wraps in tf.tidy - But async operations may leak tensors if exceptions thrown mid-execution - Line 1151: TODO comment about LLM summarizer suggests incomplete feature 2. **No Batch Processing** - All tools process single inputs - No batch API for multiple forward passes - Sequential processing in training (scripts/train-model.ts) 3. **Checkpoint Size Unbounded** - Memory state grows indefinitely - Pruning exists but not automatic - No checkpoint compression ## Phase 4: Production Readiness Gaps ### 4.1 Missing Production Features 1. **No Health Checks** - HTTP API exists but no `/health` endpoint - No readiness/liveness probes for containerized deployment 2. **Logging Insufficient** - Telemetry exists (ModelTelemetry class) but only in-memory - No structured logging to files/external systems - No log rotation 3. **No Rate Limiting** - Tools can be called unlimited times - No protection against resource exhaustion 4. **Configuration Management Weak** - Hard-coded defaults throughout - No environment variable validation - No configuration schema documentation ### 4.2 Security Concerns 1. **No Input Sanitization** - Text inputs not sanitized before encoding - File paths in load_checkpoint not validated (path traversal risk) 2. **Sensitive Data in Checkpoints** - Memory states saved as plain JSON - No encryption at rest 3. **No Authentication** - MCP server accepts all connections - No API key or token validation ## Phase 5: Token Efficiency & Optimization Opportunities ### 5.1 Response Size Optimization **Current Issues:** 1. **Verbose Responses** - `get_memory_state` returns entire memory statistics object - Could provide summary view with optional detail flag 2. **No Response Caching** - Repeated calls to same analysis recalculate - Implement LRU cache for idempotent operations 3. **Inefficient Serialization** - Memory states converted to plain arrays - Consider binary formats (Protocol Buffers, MessagePack) ### 5.2 Computation Optimization 1. **Redundant Forward Passes** - `train_step` calls `forward()` which then calls `forward()` again - Optimize to single pass 2. **Unnecessary Tensor Copies** - Multiple `clone()` operations in memory updates - Use in-place operations where safe 3. **Transformer Stack Not Optimized** - 6 layers default, each full attention - Consider sparse attention or linear attention variants per research paper ## Phase 6: Workflow & Integration Enhancements ### 6.1 Unused Workflow Components **src/workflows/** directory contains 5 files but no usage found: 1. `WorkflowOrchestrator.ts` - Appears to coordinate multi-step operations 2. `GitHubWorkflowManager.ts` - GitHub integration (why in memory server?) 3. `LintingManager.ts` - Code quality integration 4. `FeedbackProcessor.ts` - User feedback loop 5. `WorkflowUtils.ts` - Helper functions **Action Required:** Document intended use cases or remove if obsolete ### 6.2 MCP Protocol Compliance **Validation Needed:** 1. Verify JSON-RPC 2.0 error codes match spec 2. Test with MCP SDK test suite if available 3. Validate tool schema format against MCP specification 4. Ensure progress notifications for long-running operations ## Prioritized Action Items ### Immediate (Week 1) 1. **Reconcile Documentation** - Update docs/api/README.md with actual tool list - Fix tool count discrepancy (10 vs 16 claimed) - Align IMPLEMENTATION_COMPLETE.md with PRODUCTION_READINESS_ANALYSIS.md 2. **Fix Critical Bugs** - Add type guards for forward_pass input parameter - Fix autoSave silent failure path - Validate embedding dimensions on checkpoint load 3. **Add Basic Integration Tests** - Create test_mcp_integration.ts covering all 10 tools - Test init → forward → train → save → load cycle ### Short-term (Weeks 2-4) 1. **Implement Missing Research Concepts** - Add momentum state (S_t) to IMemoryState interface - Implement token flow tracking in memory updates - Enable hierarchical memory promotion/demotion 2. **Production Hardening** - Add /health endpoint - Implement structured logging with rotation - Add input sanitization and path validation - Create configuration validation schema 3. **Performance Optimization** - Eliminate redundant forward passes in training - Implement tensor operation in-place optimizations - Add LRU cache for get_memory_state ### Long-term (Months 2-3) 1. **Advanced Features from Paper** - Implement learnable forgetting gate (α_t parameter) - Add deep neural memory module option - Integrate momentum-based update rule from Equations 32-33 2. **Scalability Enhancements** - Batch processing API - Distributed training support - Checkpoint compression and streaming 3. **Ecosystem Integration** - Document or remove workflow components - Create example integrations with popular LLMs - Build Cursor IDE specific optimizations ## Success Metrics - **Documentation:** 100% accuracy (0 outdated references) - **Implementation:** All research paper core concepts present - **Testing:** >80% code coverage including integration tests - **Performance:** <100ms tool response time (95th percentile) - **Reliability:** All tools have consistent error handling - **Security:** Input validation + checkpoint encryption implemented - **Compliance:** Pass MCP SDK validation suite