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It provides structured reasoning capabilities through recursive refinement of logical arguments.\n\n[![Python 3.13+](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/)\n[![ONNX Runtime](https://img.shields.io/badge/ONNX--Runtime-1.23+-green.svg)](https://onnxruntime.ai/)\n[![MCP Compatible](https://img.shields.io/badge/MCP-Compatible-orange.svg)](https://modelcontextprotocol.io/)\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Features](#features)\n- [Architecture](#architecture)\n- [Installation](#installation)\n- [Configuration](#configuration)\n- [API Reference](#api-reference)\n  - [Tool Specifications](#tool-specifications)\n  - [MCP Resources](#mcp-resources)\n  - [REST API Endpoints](#rest-api-endpoints)\n- [Integration Options](#integration-options)\n- [Usage Examples](#usage-examples)\n- [Model Training](#model-training)\n- [Performance](#performance)\n- [Troubleshooting](#troubleshooting)\n- [Research Foundation](#research-foundation)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Overview\n\nTrmEngine is a comprehensive recursive reasoning server that implements logic validation using latent space computation. It provides structured reasoning capabilities through recursive refinement of logical arguments and serves as a complete MCP (Model Context Protocol) server.\n\n## Features\n\n### 🔧 **Core Capabilities**\n- **Recursive Reasoning**: Advanced logical validation through latent space computation\n- **Confidence Scoring**: Quantitative assessment of reasoning quality and convergence\n- **Domain Adaptation**: Specialized reasoning for mathematical, logical, and causal domains\n- **Action Verification**: Gatekeeper functionality for proposed conclusions\n\n### 🛠️ **MCP Integration**\n- **3 Specialized Tools**: `logical_recursion`, `state_verification`, `latent_projection`\n- **5 Rich Resources**: Documentation, configuration, health status, examples, and model information\n- **JSON-RPC 2.0**: Full protocol compliance with proper error handling\n- **Cursor Compatible**: Seamless integration with Cursor and other MCP clients\n\n### 📊 **Performance**\n- **Low Latency**: 150-300ms typical response times\n- **Memory Efficient**: \u003c 100MB memory usage\n- **Scalable**: Configurable recursion depth and embedding dimensions\n- **Reliable**: Comprehensive error handling and validation\n\n### 🎯 **Use Cases**\n- **LLM Validation**: Verify reasoning accuracy in language models\n- **Mathematical Proofs**: Validate logical consistency in mathematical arguments\n- **Research Analysis**: Assess quality of logical arguments in academic work\n- **Decision Support**: Provide confidence scores for critical reasoning tasks\n\n## Architecture\n\n### System Design\n\nTrmEngine implements the official Model Context Protocol (MCP) with structured reasoning tools:\n\n```\nLanguage Model → MCP Protocol → Validation Engine → Recursive Reasoning → Response\n```\n\nThe system uses JSON-RPC 2.0 for communication, providing three specialized reasoning tools with comprehensive input validation and recursive logic processing.\n\n### Core Components\n\n#### MCPServer\n\n- **Protocol Implementation**: Official MCP specification with JSON-RPC 2.0\n- **Transport Layer**: Stdio-based communication with proper message framing\n- **Tool Registry**: Manages 3 specialized reasoning tools with schema validation\n- **Resource System**: Provides 5 rich resources for documentation and monitoring\n- **Client Management**: Handles capability negotiation and connection lifecycle\n- **Error Handling**: Robust error responses with proper JSON-RPC formatting\n\n#### TRMLogicCore\n\n- **Recursive Engine**: Implements Tiny Recursive Model logic for reasoning\n- **Latent Computation**: Vector-based processing with convergence analysis\n- **Confidence Metrics**: Quantitative assessment using stability and entropy measures\n- **Performance Optimization**: Efficient numpy-based operations\n- **Flexible Configuration**: Adjustable recursion depth and embedding dimensions\n\n#### TextLatentBridge\n\n- **Text Encoding**: Converts natural language to semantic vector representations\n- **Feature Extraction**: Identifies logical structures and reasoning patterns\n- **Latent Decoding**: Transforms vector states to human-readable assessments\n- **Domain Adaptation**: Specialized processing for mathematical, logical, and causal reasoning\n- **Quality Analysis**: Automated evaluation of argument coherence and validity\n\n#### Validation Layer\n\n- **Schema Validation**: Zod-inspired input validation with detailed error messages\n- **Type Safety**: Comprehensive type checking for all API parameters\n- **Constraint Enforcement**: Business logic validation and bounds checking\n- **Security**: Input sanitization and safe parameter handling\n- **Recovery**: Graceful error handling with informative feedback\n\n## Installation\n\n### System Requirements\n\n- Python 3.13 or higher\n- uv package manager\n- Linux distribution (tested on Debian 13)\n- MCP-compatible client (Claude Desktop, etc.)\n\n### Package Manager Installation\n\nInstall uv if not already available:\n\n```bash\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n### Project Setup\n\n1. Clone or download the project files:\n\n```bash\ncd /path/to/TrmEngine\n```\n\n2. Install dependencies:\n\n```bash\nuv sync\n```\n\n3. Verify installation:\n\n```bash\nuv run python -c \"import trmengine; print('Installation successful')\"\n```\n\n### MCP Server Setup\n\nThe TrmEngine MCP server uses stdio transport for communication with MCP clients. Configure your MCP client (like Claude Desktop) to use the server:\n\n```json\n{\n  \"mcpServers\": {\n    \"trm-engine\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--project\", \"/path/to/TrmEngine\", \"python\", \"-m\", \"trmengine.server\"],\n      \"env\": {\n        \"TRM_MODEL_PATH\": \"./models/trm_v1.onnx\",\n        \"RECURSION_LIMIT\": \"16\",\n        \"EMBEDDING_DIM\": \"512\"\n      }\n    }\n  }\n}\n```\n\n### Alternative Installation Methods\n\n#### Editable Installation\n\nFor development work:\n\n```bash\nuv pip install -e .\n```\n\n#### Distribution Build\n\nTo create distributable packages:\n\n```bash\nuv build\n```\n\nThis creates wheel and source distribution files in the `dist/` directory.\n\n## Configuration\n\n### Environment Variables\n\nTrmEngine behavior can be customized through environment variables:\n\n| Variable            | Default                  | Description                          |\n| ------------------- | ------------------------ | ------------------------------------ |\n| `TRM_MODEL_PATH`  | `./models/trm_v1.onnx` | Path to ONNX model file (optional)   |\n| `RECURSION_LIMIT` | `16`                   | Number of recursive refinement steps |\n| `EMBEDDING_DIM`   | `512`                  | Dimension of latent embedding space  |\n\n### Claude Desktop Integration\n\nTo integrate with Claude Desktop via MCP:\n\n1. Locate your MCP configuration file:\n\n   - Linux/macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`\n   - Windows: `%APPDATA%/Claude/claude_desktop_config.json`\n2. Add the TrmEngine server configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"trm-engine\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--project\", \"/absolute/path/to/TrmEngine\", \"python\", \"-m\", \"trmengine.main\"],\n      \"env\": {\n        \"TRM_MODEL_PATH\": \"./models/trm_v1.onnx\",\n        \"RECURSION_LIMIT\": \"16\",\n        \"EMBEDDING_DIM\": \"512\"\n      }\n    }\n  }\n}\n```\n\n3. Restart Claude Desktop to load the configuration.\n\n### Model Configuration\n\nTrmEngine uses a functional implementation by default. To use a custom ONNX model:\n\n1. Place your model file in the project directory or set the `TRM_MODEL_PATH` environment variable\n2. Ensure the model accepts inputs: `x` (512), `y` (512), `z` (512)\n3. The model should output: `z_new` (512), `y_new` (512)\n\nIf no model file is found, TrmEngine automatically uses its built-in functional implementation.\n\n## API Reference\n\n### Tool Specifications\n\n#### logical_recursion\n\nPerforms recursive reasoning validation on logical arguments.\n\n**Input Parameters:**\n\n| Parameter           | Type    | Required | Default | Description                          |\n| ------------------- | ------- | -------- | ------- | ------------------------------------ |\n| `thought`         | string  | Yes      | -       | The logical argument to validate     |\n| `context`         | string  | No       | null    | Additional context for the reasoning |\n| `recursion_depth` | integer | No       | 16      | Number of recursive refinement steps |\n\n**Response Format:**\n\n```json\n{\n  \"original_thought\": \"input argument text\",\n  \"context\": \"provided context\",\n  \"refined_state\": {\n    \"status\": \"LOGIC_VERIFIED | LOGIC_WARNING | LOGIC_UNCERTAIN\",\n    \"confidence\": 0.0,\n    \"convergence_score\": 0.0,\n    \"magnitude\": 0.0,\n    \"stability\": 0.0,\n    \"entropy\": 0.0,\n    \"logical_quality\": 0.0\n  },\n  \"assessment\": \"textual assessment of reasoning quality\",\n  \"recursion_steps\": 16,\n  \"latent_dimensions\": 512\n}\n```\n\n**Status Values:**\n\n- `LOGIC_VERIFIED`: High confidence in logical validity\n- `LOGIC_WARNING`: Moderate confidence, potential issues\n- `LOGIC_UNCERTAIN`: Low confidence, significant concerns\n\n#### state_verification\n\nValidates proposed actions against previously verified reasoning states.\n\n**Input Parameters:**\n\n| Parameter             | Type   | Required | Default | Description                    |\n| --------------------- | ------ | -------- | ------- | ------------------------------ |\n| `proposed_action`   | string | Yes      | -       | Action or conclusion to verify |\n| `trm_refined_state` | object | Yes      | -       | Output from logical_recursion  |\n| `threshold`         | float  | No       | 0.85    | Minimum confidence threshold   |\n\n**Response Format:**\n\n```json\n{\n  \"proposed_action\": \"action description\",\n  \"verification_result\": \"VERIFIED | CONDITIONAL | REJECTED\",\n  \"approved\": true,\n  \"confidence_score\": 0.0,\n  \"threshold_used\": 0.85,\n  \"message\": \"human-readable verification result\",\n  \"trm_state_summary\": {\n    \"status\": \"original reasoning status\",\n    \"original_assessment\": \"original assessment text\"\n  }\n}\n```\n\n#### latent_projection\n\nApplies logical constraints to reasoning processes within specific domains.\n\n**Input Parameters:**\n\n| Parameter               | Type   | Required | Default | Description                           |\n| ----------------------- | ------ | -------- | ------- | ------------------------------------- |\n| `constraints`         | string | Yes      | -       | Logical constraints to apply          |\n| `target_domain`       | string | No       | null    | Domain: mathematical, logical, causal |\n| `projection_strength` | float  | No       | 0.8     | Strength of constraint application    |\n\n**Response Format:**\n\n```json\n{\n  \"constraints\": \"constraint text\",\n  \"target_domain\": \"domain name\",\n  \"projection_strength\": 0.8,\n  \"projected_latent\": {\n    \"shape\": [512],\n    \"magnitude\": 0.0,\n    \"stability\": 0.0\n  },\n  \"refined_state\": {\n    \"status\": \"constraint validation status\",\n    \"confidence\": 0.0,\n    \"convergence_score\": 0.0\n  },\n  \"proven_path\": \"textual summary of constraint validation\",\n  \"assessment\": \"detailed assessment of constraint application\"\n}\n```\n\n### MCP Resources\n\nTrmEngine provides 5 rich resources for enhanced discoverability and documentation access:\n\n#### Available Resources\n\n| URI | MIME Type | Description |\n|-----|-----------|-------------|\n| `trm://docs/readme` | `text/markdown` | Complete TrmEngine documentation |\n| `trm://config/current` | `application/json` | Current server configuration |\n| `trm://status/health` | `application/json` | Server health and performance metrics |\n| `trm://examples/basic` | `application/json` | Usage examples and test cases |\n| `trm://model/info` | `application/json` | Model capabilities and information |\n\n#### Resource Access\n\n**List Resources:**\n```bash\n# Via MCP\n{\"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"resources/list\"}\n\n# Response\n{\n  \"result\": {\n    \"resources\": [\n      {\n        \"uri\": \"trm://docs/readme\",\n        \"mimeType\": \"text/markdown\",\n        \"name\": \"TrmEngine README\",\n        \"description\": \"Complete TrmEngine documentation and usage guide\"\n      }\n      // ... more resources\n    ]\n  }\n}\n```\n\n**Read Resource:**\n```bash\n# Via MCP\n{\"jsonrpc\": \"2.0\", \"id\": 2, \"method\": \"resources/read\", \"params\": {\"uri\": \"trm://config/current\"}}\n\n# Response\n{\n  \"result\": {\n    \"contents\": [{\n      \"uri\": \"trm://config/current\",\n      \"mimeType\": \"application/json\",\n      \"text\": \"{\\\"model_path\\\": \\\"default (functional)\\\", \\\"recursion_limit\\\": 16, ...}\"\n    }]\n  }\n}\n```\n\n#### Resource Content Examples\n\n**Configuration Resource:**\n```json\n{\n  \"model_path\": \"default (functional)\",\n  \"recursion_limit\": 16,\n  \"embedding_dim\": 512,\n  \"server_version\": \"0.1.0\",\n  \"capabilities\": [\"tools\", \"resources\", \"prompts\"],\n  \"tools_count\": 3,\n  \"resources_count\": 5\n}\n```\n\n**Health Status Resource:**\n```json\n{\n  \"status\": \"healthy\",\n  \"timestamp\": \"2026-01-10T16:00:00Z\",\n  \"uptime\": \"00:30:00\",\n  \"memory_usage\": \"85MB\",\n  \"active_connections\": 1,\n  \"total_requests\": 42,\n  \"error_rate\": 0.0,\n  \"last_error\": null\n}\n```\n\n### REST API Endpoints\n\nWhen running the API server (`uv run python -m trmengine.api_server`), the following HTTP endpoints are available:\n\n- `GET /health` - Server health check\n- `GET /docs` - Interactive API documentation\n- `POST /logical-recursion` - Logical reasoning validation\n- `POST /state-verification` - Action verification\n- `POST /latent-projection` - Constraint projection\n\nAll POST endpoints accept JSON request bodies matching the parameter specifications above.\n\n## Integration Options\n\nTrmEngine supports multiple integration approaches depending on your use case and platform requirements.\n\n### MCP Protocol (Cursor \u0026 Claude Desktop) ⭐ **RECOMMENDED**\n\n**Best for:** Native integration with Cursor, Claude Desktop, and other MCP-compatible clients\n\n**Capabilities:**\n- ✅ **3 Tools**: `logical_recursion`, `state_verification`, `latent_projection`\n- ✅ **5 Resources**: Documentation, configuration, health status, examples, model info\n- ✅ **Full MCP Support**: JSON-RPC 2.0 with proper error handling\n\n**Setup:**\n\n1. Copy the `mcp.json` configuration file to your MCP config directory:\n   - **Cursor**: `~/.cursor/mcp.json`\n   - **Claude Desktop**: `~/Library/Application Support/Claude/claude_desktop_config.json`\n\n2. The configuration file should contain:\n```json\n{\n  \"mcpServers\": {\n    \"trm-engine\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--project\", \"/path/to/TrmEngine\", \"python\", \"-m\", \"trmengine.main\"],\n      \"env\": {\n        \"TRM_MODEL_PATH\": \"/path/to/models/trm_v1.onnx\",\n        \"RECURSION_LIMIT\": \"16\",\n        \"EMBEDDING_DIM\": \"512\"\n      }\n    }\n  }\n}\n```\n\n**What Cursor/Claude Will See:**\n- **Tools**: 3 reasoning tools with full schema documentation\n- **Resources**: 5 discoverable resources for documentation and status\n- **Health**: Real-time server monitoring and diagnostics\n- **Examples**: Built-in usage examples and test cases\n\n**Usage:**\n```bash\n# Tools are automatically available in Cursor/Claude conversations\n# Resources can be accessed via MCP resource browsing\n```\n\n### Python Client Library\n\n**Best for:** Direct integration in Python applications\n\n**Setup:**\n\n```python\nfrom trmengine.standalone_client import get_trm_client\n```\n\n**Usage:**\n\n```python\nimport asyncio\nfrom trmengine.standalone_client import get_trm_client\n\nasync def validate_reasoning():\n    client = get_trm_client()\n\n    result = await client.logical_recursion(\n        thought=\"If all humans are mortal and Socrates is human, then Socrates is mortal\",\n        context=\"syllogistic reasoning\"\n    )\n\n    print(f\"Status: {result['refined_state']['status']}\")\n    print(f\"Confidence: {result['refined_state']['confidence']:.3f}\")\n\nasyncio.run(validate_reasoning())\n```\n\n### REST API Server\n\n**Best for:** Integration with any programming language or web application\n\n**Setup:**\n\n```bash\nuv run python -m trmengine.api_server\n```\n\n**Usage:**\n\n```python\nimport requests\n\n# Validate logical reasoning\nresponse = requests.post(\"http://localhost:8000/logical-recursion\", json={\n    \"thought\": \"All men are mortal. Socrates is a man. Therefore Socrates is mortal.\",\n    \"context\": \"syllogistic logic\"\n})\n\nresult = response.json()\nprint(f\"Status: {result['refined_state']['status']}\")\nprint(f\"Confidence: {result['refined_state']['confidence']:.3f}\")\n```\n\n### Direct Component Usage\n\n**Best for:** Custom implementations and advanced use cases\n\n**Setup:**\n\n```python\nfrom trmengine.core import TRMLogicCore, TextLatentBridge\n\ncore = TRMLogicCore()\nbridge = TextLatentBridge()\n```\n\n## Usage Examples\n\n### Basic Reasoning Validation\n\nValidate a logical argument:\n\n```python\nfrom trmengine.standalone_client import get_trm_client\nimport asyncio\n\nasync def example():\n    client = get_trm_client()\n\n    # Validate syllogistic reasoning\n    result = await client.logical_recursion(\n        thought=\"All mammals are animals. Dogs are mammals. Therefore dogs are animals.\",\n        context=\"categorical syllogism\"\n    )\n\n    print(f\"Validation: {result['refined_state']['status']}\")\n    print(f\"Confidence: {result['refined_state']['confidence']:.3f}\")\n\nasyncio.run(example())\n```\n\n### Multi-Step Reasoning Chain\n\nCombine multiple validation steps:\n\n```python\nasync def reasoning_chain():\n    client = get_trm_client()\n\n    # Step 1: Validate premise\n    premise_result = await client.logical_recursion(\n        thought=\"All mathematical theorems require proof.\",\n        context=\"mathematical logic\"\n    )\n\n    # Step 2: Apply domain constraints\n    constraints = await client.latent_projection(\n        constraints=\"Proofs must use valid logical inference rules\",\n        target_domain=\"mathematical\"\n    )\n\n    # Step 3: Verify conclusion\n    verification = await client.state_verification(\n        proposed_action=\"Accept that theorems need proofs\",\n        trm_refined_state=premise_result\n    )\n\n    print(f\"Chain result: {verification['verification_result']}\")\n\nasyncio.run(reasoning_chain())\n```\n\n### Web Application Integration\n\nIntegrate with web frameworks:\n\n```python\n# Flask example\nfrom flask import Flask, request, jsonify\nimport requests\n\napp = Flask(__name__)\n\n@app.route('/validate-logic', methods=['POST'])\ndef validate_logic():\n    data = request.json\n\n    # Call TrmEngine API\n    response = requests.post(\"http://localhost:8000/logical-recursion\", json=data)\n    result = response.json()\n\n    return jsonify(result)\n\nif __name__ == '__main__':\n    app.run()\n```\n\n### Batch Processing\n\nProcess multiple arguments:\n\n```python\nasync def batch_validation():\n    client = get_trm_client()\n\n    arguments = [\n        \"All birds can fly. Penguins are birds. Therefore penguins can fly.\",\n        \"2 + 2 = 4\",\n        \"This statement is false.\"\n    ]\n\n    for arg in arguments:\n        result = await client.logical_recursion(thought=arg)\n        quality = result['refined_state']['logical_quality']\n        print(f\"'{arg[:30]}...': Quality {quality:.3f}\")\n\nasyncio.run(batch_validation())\n```\n\n### Error Handling\n\nHandle validation failures gracefully:\n\n```python\nasync def robust_validation():\n    client = get_trm_client()\n\n    try:\n        result = await client.logical_recursion(\n            thought=\"Complex logical argument here...\",\n            context=\"advanced reasoning\"\n        )\n\n        if result.get('error'):\n            print(f\"Validation error: {result['error']}\")\n            return None\n\n        status = result['refined_state']['status']\n        confidence = result['refined_state']['confidence']\n\n        if status == 'LOGIC_VERIFIED' and confidence \u003e 0.8:\n            print(\"Strong logical argument\")\n        elif status == 'LOGIC_WARNING':\n            print(\"Argument needs review\")\n        else:\n            print(\"Weak logical argument\")\n\n    except Exception as e:\n        print(f\"Processing error: {e}\")\n\nasyncio.run(robust_validation())\n```\n\n## Model Training\n\nTrmEngine includes a functional TRM implementation by default. For advanced use cases, you can train custom models using the Samsung TinyRecursiveModels framework.\n\n### Using Pre-trained Models\n\n1. **Download the framework:**\n\n   ```bash\n   git clone https://github.com/SamsungSAILMontreal/TinyRecursiveModels.git\n   cd TinyRecursiveModels\n   pip install -r requirements.txt\n   ```\n2. **Train a model:**\n\n   ```bash\n   # Example training command for ARC tasks\n   python pretrain.py \\\n     --task arc1 \\\n     --model_size small \\\n     --max_epochs 100 \\\n     --batch_size 32\n   ```\n3. **Export trained model to ONNX:**\n\n   ```python\n   import torch\n   from models.trm import TinyRecursiveModel\n\n   # Load trained model\n   model = TinyRecursiveModel(embed_dim=512, hidden_dim=256)\n   model.load_state_dict(torch.load('path/to/trained_model.pth'))\n   model.eval()\n\n   # Create dummy inputs matching TrmEngine interface\n   dummy_input = {\n       'x': torch.randn(1, 512),\n       'y': torch.randn(1, 512),\n       'z': torch.randn(1, 512)\n   }\n\n   # Export to ONNX\n   torch.onnx.export(\n       model,\n       (dummy_input['x'], dummy_input['y'], dummy_input['z']),\n       'trm_v1.onnx',\n       input_names=['x', 'y', 'z'],\n       output_names=['z_new', 'y_new'],\n       dynamic_axes={\n           'x': {0: 'batch_size'},\n           'y': {0: 'batch_size'},\n           'z': {0: 'batch_size'},\n           'z_new': {0: 'batch_size'},\n           'y_new': {0: 'batch_size'}\n       },\n       opset_version=11\n   )\n   ```\n4. **Configure TrmEngine:**\n\n   ```bash\n   export TRM_MODEL_PATH=\"/path/to/trm_v1.onnx\"\n   ```\n\n### Model Requirements\n\nFor compatibility with TrmEngine, ONNX models must:\n\n- Accept three inputs: `x`, `y`, `z` (each shape [batch_size, 512])\n- Return two outputs: `z_new`, `y_new` (each shape [batch_size, 512])\n- Use ONNX opset version 11 or compatible\n\n### Built-in Implementation\n\nBy default, TrmEngine uses a functional implementation that provides:\n\n- Recursive reasoning without external model dependencies\n- Content-aware logical analysis\n- Confidence scoring based on convergence analysis\n- Support for domain-specific adaptations\n\nThe built-in implementation is suitable for most use cases and does not require model training.\n\n## Performance\n\n### System Requirements\n\n- **Memory**: 100MB RAM minimum, 200MB recommended\n- **Storage**: 50MB for installation\n- **CPU**: Any modern processor (no GPU required)\n- **Network**: None (runs locally)\n\n### Performance Characteristics\n\n| Operation                  | Typical Time | Memory Usage |\n| -------------------------- | ------------ | ------------ |\n| Text encoding              | \u003c 5ms        | \u003c 10MB       |\n| Single recursion step      | ~10ms        | \u003c 50MB       |\n| Full validation (16 steps) | ~150ms       | \u003c 100MB      |\n| API request/response       | \u003c 200ms      | \u003c 50MB       |\n| MCP resource access        | \u003c 50ms       | \u003c 20MB       |\n| Health check (ping)        | \u003c 10ms       | \u003c 5MB        |\n\n### Configuration Impact\n\n- **Recursion Depth**: Higher values improve accuracy but increase processing time\n- **Embedding Dimension**: Larger dimensions provide better semantic understanding\n- **Model Type**: ONNX models may be faster than built-in implementation\n\n### Benchmark Results\n\nTested on Debian 13 with Intel i5 processor:\n\n```\nLogical validation (16 steps): 145ms average\nState verification: 89ms average\nLatent projection: 132ms average\nMemory usage: 78MB peak\n```\n\n## Troubleshooting\n\n### Installation Issues\n\n#### Dependency Installation Fails\n\n```bash\n# Clear cache and reinstall\nuv cache clean\nuv sync --reinstall\n```\n\n#### Python Version Compatibility\n\nEnsure Python 3.13+ is available:\n\n```bash\npython --version\nuv python install 3.13\nuv python pin 3.13\n```\n\n### Runtime Issues\n\n#### Model Loading Problems\n\nIf you encounter model loading errors:\n\n```bash\n# Check if model file exists\nls -la $TRM_MODEL_PATH\n\n# Verify ONNX model integrity\npython -c \"import onnxruntime as ort; ort.InferenceSession('$TRM_MODEL_PATH')\"\n```\n\n#### API Server Won't Start\n\nCheck for port conflicts:\n\n```bash\n# Check if port 8000 is available\nnetstat -tlnp | grep :8000\n\n# Use different port\nPORT=8001 uv run python -m trmengine.api_server\n```\n\n#### Memory Issues\n\nIf you experience memory problems:\n\n```bash\n# Reduce embedding dimension\nexport EMBEDDING_DIM=256\n\n# Reduce recursion depth\nexport RECURSION_LIMIT=8\n```\n\n### Integration Issues\n\n#### MCP Configuration Problems\n\nValidate MCP configuration:\n\n```bash\n# Check JSON syntax\npython -m json.tool ~/Library/Application\\ Support/Claude/claude_desktop_config.json\n\n# Verify paths exist\nls -la /path/to/TrmEngine\n```\n\n#### REST API Connection Issues\n\nTest API connectivity:\n\n```bash\n# Check if server is running\ncurl http://localhost:8000/health\n\n# Test with verbose output\ncurl -v http://localhost:8000/logical-recursion \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"thought\": \"test\"}'\n```\n\n### Validation Issues\n\n#### Unexpected Validation Results\n\nDebug validation process:\n\n```python\n# Enable detailed logging\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\n\n# Test with simple input\nfrom trmengine.standalone_client import get_trm_client\nimport asyncio\n\nasync def debug():\n    client = get_trm_client()\n    result = await client.logical_recursion(\"2 + 2 = 4\")\n    print(\"Debug result:\", result)\n\nasyncio.run(debug())\n```\n\n#### Confidence Scores Not As Expected\n\nCheck input quality:\n\n```python\n# Test with different inputs\ninputs = [\n    \"Clear logical statement\",\n    \"Ambiguous statement\",\n    \"Contradictory statement\"\n]\n\nfor text in inputs:\n    result = await client.logical_recursion(text)\n    print(f\"'{text}': {result['refined_state']['confidence']:.3f}\")\n```\n\n### Development Issues\n\n#### Testing Fails\n\nRun tests individually:\n\n```bash\n# Run specific test\nuv run python test_installation.py\n\n# Run integration test\nuv run python integration_demo.py\n```\n\n#### Import Errors During Development\n\nEnsure proper Python path:\n\n```bash\n# Add project to path\nexport PYTHONPATH=\"/path/to/TrmEngine/src:$PYTHONPATH\"\n\n# Test import\npython -c \"import trmengine; print('OK')\"\n```\n\n### Performance Optimization\n\n#### Slow Response Times\n\nOptimize configuration:\n\n```bash\n# Use smaller embedding dimension\nexport EMBEDDING_DIM=256\n\n# Reduce recursion for faster responses\nexport RECURSION_LIMIT=8\n\n# Use ONNX model if available (may be faster)\nexport TRM_MODEL_PATH=\"/path/to/model.onnx\"\n```\n\n#### High Memory Usage\n\nMonitor and optimize:\n\n```bash\n# Check memory usage\nps aux | grep trmengine\n\n# Use lighter configuration\nexport EMBEDDING_DIM=256\nexport RECURSION_LIMIT=8\n```\n\n## Universal LLM Compatibility\n\n**TrmEngine works with ANY LLM**, not just Claude! Here are the integration options:\n\n### **For Grok, GPT, or Any LLM**\n\n```bash\n# Start TrmEngine API server\ncd TrmEngine \u0026\u0026 uv run python -m trmengine.api_server\n\n# Then any LLM can make HTTP requests:\ncurl -X POST http://localhost:8000/logical-recursion \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"thought\": \"Your reasoning here\", \"context\": \"Optional context\"}'\n```\n\n### **For Python Applications**\n\n```python\nfrom trmengine.standalone_client import get_trm_client\nimport asyncio\n\nclient = get_trm_client()\nresult = await client.logical_recursion(\"Your thought here\")\n```\n\n### **For Claude Desktop (MCP)**\n\n- Automatically configured in your `mcp.json`\n- Native tool integration within Claude Desktop\n\n### **For Web Applications**\n\n- Deploy the REST API server\n- Any frontend can call TrmEngine via HTTP\n\n### **Integration Demo**\n\nRun `uv run python integration_demo.py` to see all methods working!\n\n---\n\n## Research Foundation\n\nThis implementation is based on Samsung SAIL's research:\n\n- **Paper**: [\u0026#34;Less is More: Recursive Reasoning with Tiny Networks\u0026#34;](https://arxiv.org/abs/2510.04871)\n- **Code**: [SamsungSAILMontreal/TinyRecursiveModels](https://github.com/SamsungSAILMontreal/TinyRecursiveModels)\n- **Video**: [Tiny AI Beats Giants! – Recursive Reasoning with Tiny Networks (TRM) Explained](https://www.youtube.com/watch?v=RujtTutVUaE)\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests if applicable\n5. Submit a pull request\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Summary\n\nTrmEngine is a production-ready MCP server that provides advanced logical reasoning capabilities:\n\n- **🧠 Recursive Reasoning**: Based on Samsung's Tiny Recursive Model research\n- **🔧 3 Specialized Tools**: Logical validation, action verification, constraint projection\n- **📚 5 Rich Resources**: Documentation, configuration, health monitoring, examples, model info\n- **⚡ High Performance**: \u003c 200ms response times, \u003c 100MB memory usage\n- **🔒 Robust Validation**: Comprehensive input validation and error handling\n- **🔌 MCP Compliant**: Full JSON-RPC 2.0 implementation with proper protocol support\n- **🖥️ Cursor Ready**: Seamless integration with Cursor and Claude Desktop\n\n**Ready to enhance your AI reasoning workflows with structured logical validation!** 🚀\n\n## Acknowledgments\n\n- **Samsung SAIL Montreal** for the Tiny Recursive Model research\n- **Model Context Protocol** for the standardized tool integration framework\n- **ONNX Runtime** for efficient model inference\n\n---\n\n**Raison d'être**: A high-precision logic offloading server that replaces verbose Chain-of-Thought (CoT) with high-speed latent recursion.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F1999azzar%2Ftrmengine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F1999azzar%2Ftrmengine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F1999azzar%2Ftrmengine/lists"}