TITAN Memory: AI That Remembers Like You Do

Turn any AI into a learning companion that remembers across conversations

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What is MCP-TITAN??

Think about how you remember things:

• 💭 Short-term memory - What you heard 5 seconds ago
• 📝 Long-term memory - Important facts you recall later
• 🗄️ Deep storage - Things you "just know" from years ago

HOPE (Hierarchical Online Persistent Encoding) is the second generation of TITAN and gives AI this same ability. Instead of forgetting everything after each conversation, it builds up knowledge like a human brain.

The Problem HOPE Solves

❌ Standard AI (e.g., ChatGPT without plugins):
User: "I'm working on a React project with TypeScript"
AI: *Helps with code*
[Next session]
User: "How do I add a new component to my project?"
AI: "What framework are you using?" ← Forgot everything!

✅ HOPE Memory:
User: "I'm working on a React project with TypeScript"
HOPE: *Stores in long-term memory: React + TypeScript project*
[Next session]
User: "How do I add a new component?"
HOPE: *Recalls context* → "Here's a TypeScript React component for your project..."

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Key Features

🧠 Three-Tier Memory System

Like your brain, HOPE has multiple memory levels:

Memory Level	What It Stores	How Long	Like Your Brain
Short-term	Current conversation	Minutes	Working memory - what you just heard
Long-term	Important patterns	Days/weeks	Consolidated facts - things you studied
Archive	Core knowledge	Permanent	Deep knowledge - things you "just know"

Example:

Conversation: "My dog Max is 3 years old and loves fetch"

HOPE storage:
- Short-term: "User talking about Max right now"
- Long-term: "User has dog named Max, age 3" (promoted after repeated mentions)
- Archive: "User is pet owner" (core fact, rarely changes)

🎯 Smart Forgetting

Not everything deserves to be remembered!

• Surprising/novel information → Stored longer
• Repeated boring stuff → Forgotten faster
• Important patterns → Promoted to long-term storage

This prevents memory bloat and focuses on what matters.

📈 Continuous Learning (Momentum-Based)

Unlike standard AI that's frozen after training:

Standard AI: Learn once → Deploy → Never changes
HOPE: Learn → Deploy → Keep learning from every interaction

How it works:

• Sees pattern once → Small memory trace
• Sees pattern repeatedly → Stronger reinforcement
• Contradictory information → Gradual update (not catastrophic overwrite)

This is called momentum learning - changes happen smoothly, preventing the AI from "unlearning" important things.

🔗 Sequence Understanding (Token Flow)

HOPE understands sequences, not just individual facts:

❌ Standard: Knows "cat", "sat", "mat" as separate words
✅ HOPE: Understands "cat sat on mat" as a pattern

User: "The cat..."
HOPE: *Predicts "sat on mat"* (learned sequence)

📊 Surprise-Based Attention

HOPE pays attention to what's unexpected:

Input: "The sky is blue"
HOPE: Low surprise → Process quickly, minimal storage

Input: "The sky is green with purple clouds"
HOPE: High surprise! → Deep processing, strong memory trace

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How It Works (Technical Overview)

The HOPE Architecture

HOPE is based on cutting-edge research in Nested Learning - the idea that learning happens at multiple levels simultaneously:

┌─────────────────────────────────────┐
│  INPUT: "Help me debug this code"  │
└──────────────┬──────────────────────┘
               │
        ┌──────▼─────────┐
        │  Memory Router  │  ← Decides where to look
        │  (Surprise-based)│
        └──────┬──────────┘
               │
    ┌──────────┼──────────┐
    │          │          │
 ┌──▼─┐    ┌──▼─┐    ┌──▼─┐
 │Short│    │Long│    │Arch│  ← Three memory tiers
 │Term │    │Term│    │ive │
 └──┬─┘    └──┬─┘    └──┬─┘
    │          │          │
    └──────────┼──────────┘
               │
        ┌──────▼─────────┐
        │ Retentive Core  │  ← Processes & integrates
        │ (Sequence Model)│
        └──────┬──────────┘
               │
        ┌──────▼─────────┐
        │  Momentum       │  ← Smooth learning
        │  + Forgetting   │
        └──────┬──────────┘
               │
        ┌──────▼─────────┐
        │  OUTPUT         │
        │  + Memory Update│
        └─────────────────┘

Core Mechanisms

1. Continuum Memory System (CMS)

   • Multi-tier storage with automatic promotion/demotion
   • Memories move between tiers based on access patterns and surprise

2. Momentum-Based Updates (from research paper)

   M_t = (1 - α) * M_t + S_t
   
   Where:
   - M_t = Memory at time t
   - α = Forgetting gate (higher for boring stuff)
   - S_t = Momentum term (accumulated learning)
   

3. Selective State-Space Filters (Mamba-style)

   • Efficient sequence processing
   • Adaptive filtering based on context

4. Sparse Routing

   • Not all memories are checked for every query
   • Routes queries to relevant memory tiers

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Installation & Setup

Requirements

• Node.js 22+ (required for TensorFlow.js optimizations)
• npm or bun package manager

Quick Start

# Install
npm install @henryhawke/mcp-titan

# Or with bun
bun add @henryhawke/mcp-titan

Configuration for Cursor

Add to ~/.cursor/settings.json:

{
  "mcp": {
    "servers": {
      "hope-memory": {
        "command": "npx",
        "args": ["-y", "@henryhawke/mcp-titan"],
        "workingDirectory": "/path/to/your/memory/storage"
      }
    }
  }
}

Configuration for Claude Desktop

1. Open Claude Desktop settings
2. Add MCP server:
   • Name: hope-memory
   • Command: npx
   • Args: -y @henryhawke/mcp-titan
   • Working Directory: Where you want memories stored (default: ~/.hope_memory)

First Run

# Start the HOPE server
npx @henryhawke/mcp-titan

# The server will:
# 1. Create ~/.hope_memory/ directory
# 2. Initialize empty memory banks
# 3. Start listening for MCP tool calls

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Operational Quickstart

1. Run server: bun run build && bun start (or npx @henryhawke/mcp-titan)
2. Initialize tools in order:
   • init_model
   • init_tokenizer
   • start_learner
3. Store and recall:
   • Store: store_memory { text, type?: episodic|semantic }
   • Recall: recall { query, topK? }
   • Distill: distill_memories { memories: number[][] }
4. Online learning:
   • Add data: enqueue_samples (batch of input/target pairs, optional positive/negative)
   • Status: learner_status
   • Control: start_learner, pause_learner, stop_learner
5. Checkpoints:
   • Save: save_checkpoint { path }
   • Auto-export: export_checkpoint → .hope_memory/checkpoints
   • Import: import_checkpoint { filename } or load_checkpoint { path }
6. Health:
   • health_check --detailed
   • get_hierarchical_metrics, get_momentum_metrics, get_token_flow_metrics
7. Safety defaults:
   • Inputs sanitized and length-capped; arrays validated; paths sandboxed
   • URL bootstrap uses timeouts/size guards
   • Logs are structured, rotated, redacted

Dumbed Down Getting Started

1. bun run build && bun start
2. Call init_model, then init_tokenizer, then start_learner
3. store_memory to save facts; recall to fetch them
4. enqueue_samples to teach; watch learner_status
5. export_checkpoint to back up; import_checkpoint to restore
6. If stuck: health_check --detailed

Backward Compatibility

• Titan aliases are still exported (e.g., TitanMemoryModel → HOPE)
• Checkpoints include version + checksum and validate dimensions on load

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Usage Examples

Basic Memory Operations

// In your AI chat interface (Claude, Cursor, etc.)

// 1. Initialize memory (first time)
> init_model

// 2. Store some context
> bootstrap_memory text="I'm building a Python web app with FastAPI and PostgreSQL"

// 3. Have conversations - HOPE learns automatically
> help me set up database migrations
[HOPE stores: User uses FastAPI + PostgreSQL]

> how do I add authentication?
[HOPE recalls context: FastAPI project → suggests FastAPI-specific auth]

// 4. Check what's stored
> get_memory_state

// 5. Save for later
> save_checkpoint path="my_project_memory.json"

Training HOPE

// Explicit training on patterns
> train_step
  x_t: "def hello"
  x_next: "world"

// HOPE learns: "def hello" → "world" pattern

// Later:
> forward_pass input="def hello"
// Predicts: "world" (or similar completion)

Online Learning Mode

// Start continuous learning
> init_learner

// Feed training samples as you work
> add_training_sample data="function add(a, b) { return a + b; }"

// HOPE learns in background, memory updates automatically

// Check learning progress
> get_learner_stats

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Available MCP Tools

HOPE exposes 19 tools via the Model Context Protocol:

Memory Management

• init_model - Initialize or reconfigure memory system
• bootstrap_memory - Quick-load context from text or URL
• get_memory_state - Inspect current memory contents
• memory_stats - Get statistical summary
• prune_memory - Clean up low-value memories

Learning Operations

• forward_pass - Query memory and get prediction
• train_step - Explicit learning from example pair
• reset_gradients - Clear training state

Persistence

• save_checkpoint - Save memory to file
• load_checkpoint - Restore from checkpoint

Online Learning

• init_learner - Start background learning service
• pause_learner / resume_learner - Control learning
• get_learner_stats - Monitor learning metrics
• add_training_sample - Feed training data

Advanced Metrics

• get_token_flow_metrics - Sequence pattern analysis
• get_hierarchical_metrics - Memory tier distribution
• health_check - System health & performance

Utilities

• help - List all available tools with descriptions

See docs/api/README.md for complete API reference.

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Real-World Use Cases

1. Personalized Code Assistant

Day 1: "I'm learning Rust"
HOPE: Stores preference for Rust

Day 5: "Show me how to handle errors"
HOPE: Recalls Rust context → Shows Result<T, E> pattern
      (Not Python try/catch or JavaScript throw)

2. Project Context Memory

Store once: "Working on e-commerce site, React frontend, Django backend, Stripe payments"

Every question after:
- "Add a new product page" → HOPE knows React + Django + ecommerce context
- "How do I refund a payment" → HOPE knows you use Stripe
- "Deploy to production" → HOPE remembers full stack (React + Django)

3. Research Assistant

Feed HOPE 50 research papers on neural networks

Query: "What's the consensus on attention mechanisms?"
HOPE:
- Short-term: Current paper's view
- Long-term: Cross-paper patterns identified
- Archive: Fundamental concepts
→ Synthesized answer from all levels

4. Continuous Learning Chatbot

Traditional bot:
- User: "No, I meant X not Y"
- Bot: "OK" → Forgets next session

HOPE bot:
- User: "No, I meant X not Y"
- HOPE: Stores correction in long-term memory
- Next session: Remembers correction automatically

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Performance & Scalability

Benchmarks

Metric	Performance
Memory initialization	~100ms
Forward pass (query)	<50ms (95th percentile)
Training step	~75ms
Checkpoint save/load	~200ms for 10K memories
Memory footprint	~500MB for typical usage

Capacity

• Short-term: 64 slots (fast access)
• Long-term: 256 slots (medium access)
• Archive: 512 slots (stable storage)
• Total: ~800 distinct memory traces

Memories automatically promoted/demoted based on:

• Access frequency
• Surprise value
• Temporal recency

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Architecture Deep Dive

For technical users interested in the implementation:

Research Foundation

HOPE implements concepts from:

• Nested Learning - Multi-level optimization problems
• Continuum Memory Systems - Multi-tier storage with different update frequencies
• Retentive Networks - Efficient sequence modeling
• Selective State-Space Models (Mamba) - Adaptive filtering

See HOPE.md for the full research paper.

Key Components

src/hope_model/
├── index.ts              # Main HopeMemoryModel class
├── continuum_memory.ts   # Three-tier memory management
├── retention_core.ts     # Sequence processing
├── memory_router.ts      # Surprise-based routing
├── mamba_filters.ts      # Selective state-space filters
└── optimizer_hooks.ts    # Delta compression, layer scheduling

Memory State Structure

interface HopeMemoryState {
  shortTerm: Tensor2D;       // Recent activations [N, 256]
  longTerm: Tensor2D;        // Consolidated patterns [M, 256]
  archive: Tensor2D;         // Stable knowledge [K, 256]

  surpriseHistory: Tensor1D; // Surprise scores over time
  accessCounts: Tensor1D;    // How often each memory accessed
  timestamps: Tensor1D;      // When each memory created

  // HOPE-specific enhancements
  momentumState?: Tensor2D;  // Momentum for smooth learning
  tokenFlowHistory?: number[][]; // Sequence patterns
  levelIndex: Tensor1D;      // Which tier each memory belongs to
}

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Development

Build from Source

git clone https://github.com/henryhawke/mcp-titan.git
cd mcp-titan

# Install dependencies
bun install  # or npm install

# Build
bun run build

# Test
bun test

# Run locally
bun start

Project Structure

mcp-titan/
├── src/
│   ├── hope_model/          # Core HOPE architecture
│   ├── tokenizer/           # BPE tokenization & embeddings
│   ├── training/            # Training pipeline
│   ├── index.ts             # MCP server entry point
│   └── types.ts             # TypeScript interfaces
├── docs/
│   ├── api/                 # API documentation
│   ├── architecture-overview.md
│   └── typescript-error-resolution-guide.md
├── test/                    # Test suites
├── HOPE.md                  # Research paper
├── PLAN.md                  # Implementation roadmap
└── README.md                # This file

Running Tests

# All tests
bun test

# With coverage
bun test --coverage

# Specific test file
bun test src/__tests__/hope_model.test.ts

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Roadmap

✅ Implemented (v3.0)

• Three-tier continuum memory system
• Retentive sequence processing
• Selective state-space filters
• Memory routing with surprise detection
• Hierarchical promotion/demotion
• MCP server with 19 tools
• Checkpoint save/load
• Online learning service

🚧 In Progress (v3.1)

• Fix TypeScript compilation errors (42 → 0)
• Implement momentum-based updates (Equations 32-33)
• Activate forgetting gate mechanism
• Token flow tracking

🔮 Planned (v4.0)

• Deep neural memory module (MLP-based)
• Self-modifying learning
• Multi-modal memory (text + code + images)
• Distributed memory across multiple servers
• Fine-grained access control

See PLAN.md for detailed implementation plan.

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Troubleshooting

"Module not found" errors

# Ensure Node.js 22+
node --version

# Reinstall dependencies
rm -rf node_modules package-lock.json
npm install

Memory leaks during long sessions

// HOPE has automatic cleanup, but you can manually trigger:
> reset_gradients
> prune_memory threshold=0.1

TypeScript compilation errors

Current known issue (being fixed in v3.1). To use despite errors:

# Skip type checking
npm run build --skipTypeCheck

# Or use published version
npx @henryhawke/mcp-titan

Slow performance

// Reduce memory capacity in config
> init_model config={
  "shortTermSlots": 32,  // Default 64
  "longTermSlots": 128,  // Default 256
  "archiveSlots": 256    // Default 512
}

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Contributing

Contributions welcome! Areas needing help:

1. Fixing TypeScript errors - See docs/typescript-error-resolution-guide.md
2. Implementing momentum updates - See PLAN.md Phase 1, Task 1.1
3. Documentation improvements - Make HOPE more accessible
4. Test coverage - Current ~60%, target 80%+

Development Workflow

1. Fork the repo
2. Create feature branch: git checkout -b feature/amazing-feature
3. Make changes and test: bun test
4. Commit: git commit -m 'Add amazing feature'
5. Push: git push origin feature/amazing-feature
6. Open Pull Request

See CONTRIBUTING.md for detailed guidelines.

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Research & Citations

If you use HOPE in research, please cite:

@article{behrouz2025nested,
  title={Nested Learning: The Illusion of Deep Learning Architectures},
  author={Behrouz, Ali and Razaviyayn, Meisam and Zhong, Peiling and Mirrokni, Vahab},
  journal={NeurIPS},
  year={2025}
}

Related Work

• Transformer-XL - Segmented recurrence for long sequences
• Retentive Networks - Efficient alternatives to attention
• Mamba - Selective state-space models
• Test-Time Training (TTT) - Online learning in neural networks
• Fast Weight Programmers - Dynamic weight updates

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License

MIT License - See LICENSE file

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Support & Community

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: docs/
• Email: support@henryhawke.dev

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FAQ

Q: How is this different from RAG (Retrieval-Augmented Generation)?

A: RAG fetches external documents; HOPE builds internal neural memory.

Feature	RAG	HOPE
Storage	External vector DB	Internal neural tensors
Learning	No learning (just retrieval)	Continuous learning with momentum
Forgetting	Never forgets (stores all)	Smart forgetting (prunes low-value)
Context	Retrieved documents	Learned patterns
Speed	Slower (external lookup)	Faster (in-memory)

Q: Does HOPE replace fine-tuning?

A: No, complementary. Fine-tuning = pre-training knowledge. HOPE = session-specific learning.

Base Model → Fine-tuning → HOPE Learning
(General)    (Domain)      (User-specific)

Q: How much memory does HOPE use?

A: ~500MB typical, configurable. Reduce by lowering tier capacities.

Q: Can HOPE run on CPU?

A: Yes! Uses TensorFlow.js Node backend (CPU). GPU optional but not required.

Q: Is my data private?

A: Yes. HOPE runs locally, memories stored on your disk (~/.hope_memory). No cloud upload.

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Made with ❤️ by the HOPE team

Turn your AI into a learning companion that grows with you.