MCP Titan

# MCP-Titan Architecture Analysis & Implementation Roadmap ## Executive Summary This document provides a comprehensive analysis of the mcp-titan architecture and presents a strategic roadmap for enhancing its capabilities with modern LLM technologies, memory-augmented transformers, and optimization techniques. --- ## 1. Current MCP-Titan Architecture Audit ### Integration Points & MCP Protocol **How Titan Plugs into External LLMs via MCP:** - **MCP Server**: Acts as a memory service provider through standardized tool calls - **Protocol Interface**: Exposes 16 sophisticated memory tools through JSON-RPC 2.0 - **Memory State Management**: Persistent neural memory across LLM interactions - **Real-time Learning**: Online updates without full model retraining **Core Architecture Components:** ```mermaid graph TD A[External LLM] -->|MCP Protocol| B[TitanMemoryServer] B --> C[TitanMemoryModel] C --> D[Encoder TF.js] C --> E[Memory Retrieval HNSW] C --> F[Decoder] D --> G[Transformer Stack] E --> H[Hierarchical Memory] F --> I[Online Update] ``` ### Abstraction Seams for Model Replacement **Primary Swap Points:** 1. **`encodeText(text: string): Promise<tf.Tensor1D>`** - Current: BPE tokenizer + embedding - Replaceable: Any text encoder (SentenceTransformers, model-specific) - Interface: String → Fixed-dimension tensor 2. **`forward(input: ITensor, state?: IMemoryState)`** - Current: Transformer stack + memory attention - Replaceable: Any sequence model (Mamba, RWKV, RetNet) - Interface: Tensor + memory state → prediction + memory update 3. **`trainStep(x_t: ITensor, x_next: ITensor, state: IMemoryState)`** - Current: MSE loss + contrastive learning - Replaceable: Any loss function + RL objectives - Interface: Current/next tensors → loss + gradients **Configurable Layers:** - Transformer stack (6 layers default, max 12) - Memory projector (768→1024 dimensions) - Similarity network (cosine similarity) - Quantization pipeline (8-bit support) ### Hardware/Runtime Constraints **Current Requirements:** - Node.js 18+ (ES modules, native fetch) - TensorFlow.js-node (CPU/GPU acceleration) - Memory: ~2-8GB RAM (depending on memory slots) - VRAM: Optional GPU acceleration via CUDA **Bottlenecks:** - TF.js tensor operations (slower than native PyTorch) - Memory serialization/deserialization overhead - Single-threaded JavaScript runtime --- ## 2. Qwen3 & DeepSeek Evaluation for Self-Hosting ### Model Specifications (Latest Known) **Qwen3 (Qwen2.5 Latest)** - **Context Length**: 128K-1M tokens - **License**: Apache 2.0 (commercial friendly) - **Sizes**: 0.5B-72B parameters - **Requirements**: 8-24GB VRAM for 7B-14B models - **Tokenizer**: Custom tokenizer (multilingual) **DeepSeek V3** - **Context Length**: 64K-128K tokens - **License**: MIT (fully open) - **Sizes**: 7B-67B parameters - **Requirements**: 16-40GB VRAM for optimal performance - **Tokenizer**: SentencePiece-based ### MCP Adapter Prototype Design ```typescript interface LLMAdapter { initialize(config: ModelConfig): Promise<void>; generate(prompt: string, memory: MemoryContext): Promise<string>; embed(text: string): Promise<number[]>; tokenize(text: string): Promise<number[]>; } class DeepSeekAdapter implements LLMAdapter { private vllm: VLLMClient; async initialize(config: ModelConfig) { // Initialize vLLM with DeepSeek model this.vllm = new VLLMClient({ model: "deepseek-ai/deepseek-llm-7b-chat", tensor_parallel_size: config.gpuCount, gpu_memory_utilization: 0.85 }); } async generate(prompt: string, memory: MemoryContext) { const enrichedPrompt = this.enrichWithMemory(prompt, memory); return await this.vllm.generate(enrichedPrompt); } } ``` ### Compatibility with Titan Contract **init_model → forward_pass Flow:** 1. **Compatible**: Both models support embeddings extraction 2. **Tokenizer Alignment**: Requires adapter layer for tokenizer differences 3. **Memory Integration**: Works through prompt enrichment + retrieval **Performance Estimates:** - **Latency**: 50-200ms (vs 500-2000ms for remote Claude) - **RAM**: 16-32GB for 7B models - **VRAM**: 8-16GB for inference - **Throughput**: 20-50 tokens/sec ### SEAL Technology Integration **SEAL (Self-Adapting Language Models) Key Features:** - Continual learning without catastrophic forgetting - Adaptive memory allocation - Online parameter updates **Integration Points with Titan:** - Replace static memory slots with adaptive allocation - Implement SEAL's continual learning for memory updates - Use SEAL's forgetting mechanisms for memory pruning --- ## 3. Native "Adaptive Memory" LLMs Timeline ### Current State & Research Progress **Transformer² (Google)** - **Status**: Research phase, no open-source release - **Timeline**: 12-18 months for academic papers, 24+ months for OSS - **Features**: Native memory attention, quadratic→linear complexity **RetNet (Microsoft)** - **Status**: Open research, some implementations available - **Timeline**: 6-12 months for production-ready versions - **Features**: Linear complexity, parallel training, recurrent inference **HyperAttention** - **Status**: Early research, proof-of-concept only - **Timeline**: 18-24 months for practical implementations - **Features**: Constant-time attention, infinite context **Mamba/State Space Models** - **Status**: Mature research, growing adoption - **Timeline**: 3-6 months for TF.js/ONNX support - **Features**: Linear complexity, strong long-range modeling ### Integration Effort Assessment ``` Gantt Chart (Next 12 Months): Q1 2025: Mamba/RWKV prototyping (Medium effort) Q2 2025: RetNet early adoption (High effort) Q3 2025: SEAL integration (Medium effort) Q4 2025: Transformer² evaluation (Low effort - if available) Effort Levels: - Low: Drop-in replacement - Medium: Architecture modifications needed - High: Significant redesign required ``` --- ## 4. Reinforcement Learning Diversity Analysis ### Literature Review Findings **Dr. GRPO (Diversity-Regularized Policy Optimization)** - Encourages diverse action selection - Reduces mode collapse in memory retrieval - Application: Memory promotion/demotion policies **Dreamer Architecture** - World model learning + policy optimization - Applicable to memory state prediction - Could model long-term memory dynamics **Integration Points in Titan:** 1. **Memory Pruning Policy** ```typescript class RLMemoryPruning { private policy: PolicyNetwork; selectMemoriesToPrune(memoryState: IMemoryState): number[] { // RL-based selection vs current LRU return this.policy.selectActions(memoryState); } } ``` 2. **Retrieval Strategy** - Current: Cosine similarity + recency - RL: Learned retrieval policy maximizing downstream performance 3. **Memory Promotion Rules** - Current: Fixed thresholds - RL: Adaptive promotion based on task performance ### Experimental Protocol **Synthetic Task Design:** - Memory-dependent question answering - Catastrophic forgetting measurement - Recall precision/recall metrics **Baseline vs RL Comparison:** - Static rules vs learned policies - Diversity metrics (memory usage distribution) - Performance on held-out tasks --- ## 5. Mamba/S4 Integration Investigation ### Architecture Replacement Strategy **Current Transformer Stack:** ```typescript // src/model.ts lines 658-688 this.transformerStack = []; for (let i = 0; i < this.config.transformerLayers; i++) { const layer = tf.sequential({ layers: [ tf.layers.dense({ units: this.config.hiddenDim }), tf.layers.layerNormalization(), // ... more transformer layers ] }); } ``` **Proposed Mamba Replacement:** ```typescript class MambaLayer { private ssm: StateSpaceModel; private conv1d: tf.layers.Layer; call(inputs: tf.Tensor): tf.Tensor { // Implement Mamba's selective state space const convOut = this.conv1d.apply(inputs); return this.ssm.forward(convOut); } } ``` ### Implementation Approaches 1. **TensorFlow.js Implementation** - Pure JS/TypeScript implementation - Pros: Native integration, same runtime - Cons: Performance limitations, complex state space ops 2. **ONNX Runtime Integration** - Pre-trained Mamba models via ONNX - Pros: Better performance, proven models - Cons: Additional dependencies, deployment complexity 3. **WebAssembly Bridge** - Compile native Mamba implementations to WASM - Pros: Near-native performance - Cons: Development complexity, larger bundle size ### Benchmarking Methodology **Metrics to Measure:** - Throughput (sequences/second) - Memory usage (peak RAM/VRAM) - Context length scaling (linear vs quadratic) - Quality on long-context tasks **Test Scenarios:** - Document summarization (16K+ tokens) - Code generation with long context - Conversational memory (multi-turn) --- ## 6. BitNet & Quantization Assessment ### Current Quantization Pipeline **Existing Implementation:** ```typescript // src/model.ts lines 2646-2776 private quantizeTensor(tensor: tf.Tensor): Uint8Array { // 8-bit quantization with per-dimension ranges const maxValue = 2 ** this.quantizationBits - 1; // ... quantization logic } ``` ### Aggressive Quantization Strategies **BitNet Integration:** 1. **Binary/Ternary Weights** - Replace float32 weights with {-1, 0, 1} - 32x memory reduction potential - Quality impact: 5-15% performance drop 2. **4-bit Quantization (QLoRA-style)** ```typescript class QuantizedMemory { private weights4bit: Uint8Array; private scales: Float32Array; quantize(weights: tf.Tensor): void { // Group-wise 4-bit quantization const groups = this.groupWeights(weights, 128); groups.forEach(group => { const scale = group.abs().max(); this.weights4bit = this.quantizeToNibbles(group, scale); this.scales.push(scale); }); } } ``` 3. **Mixed Precision Strategy** - Critical layers: FP16 - Memory tensors: 8-bit - Embeddings: 4-bit ### Benchmarking Results (Projected) | Strategy | Memory Savings | Quality Drop | Load Time | Inference Speed | |----------|---------------|--------------|-----------|----------------| | 8-bit | 75% | <2% | 40% faster | 15% slower | | 4-bit | 87.5% | 3-5% | 60% faster | 25% slower | | BitNet | 96.8% | 8-12% | 80% faster | 50% slower | **Recommendation:** 8-bit quantization meets the 30% memory savings with <5% quality loss threshold. --- ## 7. Implementation Roadmap & Recommendations ### Comparative Analysis Matrix | Solution | Cost | Latency | Effort | Licensing | Memory Savings | Quality | |----------|------|---------|--------|-----------|----------------|---------| | **Current (Claude)** | High | High | Low | Proprietary | N/A | Excellent | | **DeepSeek + 8-bit** | Low | Medium | Medium | MIT | 75% | Good | | **Qwen3 + Mamba** | Low | Low | High | Apache | 60% | Very Good | | **Native Memory LLM** | Low | Low | Low | TBD | 80% | Excellent | ### Phased Implementation Plan #### Phase 1: Self-Hosting Foundation (Next 3 months) **Primary Goal:** Reduce dependency on external APIs **Tasks:** - [ ] Implement DeepSeek adapter with vLLM backend - [ ] Integrate 8-bit quantization for memory tensors - [ ] Create MCP compatibility layer for self-hosted models - [ ] Benchmark performance vs remote Claude - [ ] Deploy on cloud infrastructure (AWS/GCP) **Deliverables:** - Working self-hosted alternative - 75% memory usage reduction - <500ms latency for typical queries - Cost reduction of 80-90% #### Phase 2: Architecture Modernization (Months 4-9) **Primary Goal:** Replace transformer backbone with modern architectures **Tasks:** - [ ] Prototype Mamba layer replacement in TF.js - [ ] Implement SEAL-style adaptive memory allocation - [ ] Add RL-based memory management policies - [ ] Create ONNX runtime bridge for better performance - [ ] Extensive benchmarking on long-context tasks **Deliverables:** - Linear complexity sequence modeling - Adaptive memory system - 5x improvement in long-context handling - Maintained or improved quality metrics #### Phase 3: Next-Generation Integration (Months 10-18) **Primary Goal:** Adopt native memory-augmented LLMs when available **Tasks:** - [ ] Evaluate Transformer² once released - [ ] Implement RetNet integration if suitable - [ ] Create unified adapter interface for multiple backends - [ ] Production deployment and monitoring - [ ] Community open-source contributions **Deliverables:** - Future-proof architecture - Multiple backend support - Production-ready system - Open-source community adoption ### Risk Assessment & Mitigation **Technical Risks:** 1. **TF.js Performance Limitations** - *Mitigation*: ONNX runtime bridge, WebAssembly optimization 2. **Model Compatibility Issues** - *Mitigation*: Robust adapter interfaces, extensive testing 3. **Memory Management Complexity** - *Mitigation*: Gradual migration, fallback mechanisms **Business Risks:** 1. **Faster Evolution of External APIs** - *Mitigation*: Hybrid approach, API + self-hosted options 2. **Resource Requirements** - *Mitigation*: Efficient quantization, cloud deployment options ### Success Metrics **Short-term (3 months):** - 80% cost reduction vs external APIs - <2x latency increase vs remote models - 75% memory usage reduction **Medium-term (9 months):** - Linear scaling for context lengths >32K - 90% memory efficiency improvement - Quality parity with current system **Long-term (18 months):** - Native memory LLM integration - 10x improvement in cost-effectiveness - Industry-leading memory-augmented system --- ## Engineering Backlog Tasks ### Immediate (Sprint 1-2) - [ ] Set up vLLM development environment - [ ] Create DeepSeek model adapter interface - [ ] Implement basic quantization for memory tensors - [ ] Write integration tests for MCP compatibility ### Short-term (Sprint 3-8) - [ ] Deploy self-hosted DeepSeek on cloud infrastructure - [ ] Benchmark latency and quality vs Claude - [ ] Implement SEAL adaptive memory allocation - [ ] Create monitoring and observability tools ### Medium-term (Sprint 9-20) - [ ] Research and prototype Mamba integration - [ ] Implement RL-based memory management - [ ] Create ONNX runtime bridge - [ ] Extensive performance optimization ### Long-term (Sprint 21+) - [ ] Evaluate and integrate next-generation memory LLMs - [ ] Production deployment and scaling - [ ] Open-source community contributions - [ ] Research novel memory architectures --- This roadmap provides a strategic path forward for mcp-titan, balancing immediate practical needs with long-term architectural evolution. The phased approach allows for incremental progress while maintaining system stability and performance.