| ---
|
| language: py
|
| tags:
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| - concept-reasoning
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| - neural-symbolic
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| - graph-neural-network
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| - GAT
|
| - self-correcting-agent
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| - code-generation
|
| - edge-ai
|
| license: mit
|
| ---
|
|
|
| # 🚀 CAT V3 Coding Agent (Graph-MoE + Self-Correcting Sandbox)
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|
|
| Welcome to the official repository for the **CAT V3 Coding Agent**. This project represents a state-of-the-art **neural-symbolic coding agent** designed for edge deployment. It decouples high-level logical path planning (System 2) from code syntax generation (System 1) and pairs them with a multi-language self-correcting execution sandbox.
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|
|
| 👉 **Model Repository**: [huggingface.co/Chaman1234/cat-v3-coding-agent](https://huggingface.co/Chaman1234/cat-v3-coding-agent)
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|
|
| ---
|
|
|
| ## 🏛️ Architecture & Core Philosophy
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|
|
| Traditional LLMs generate code token-by-token, which frequently leads to logical drift, syntax errors, and reasoning hallucinations. The **Concept Attention Transformer V3 (CAT V3)** resolves this by enforcing structural constraints:
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|
|
| ```text
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| User Query ➔ Semantic Router ➔ Specialist Expert GATs ➔ Concept Path (0% Logical Hallucinations)
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| │
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| ┌─────────────────────────── Self-Correction Loop ◄──────────────┘
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| ▼
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| Code Draft (Ollama 3B) ➔ Sandboxed Execution ➔ Success / Debug Retry
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| ```
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|
|
| ### Key Stages:
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| 1. **Query Seeding & Normalization**: The input query is cleaned by the `grammar_parser` (resolving spelling errors, normalizing units, and mapping boundary conditions).
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| 2. **Sparse Graph Mixture of Experts (Graph-MoE)**: The query is semantically routed to active specialists. For programming tasks, it routes to the **Coding GAT Specialist**.
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| 3. **Topologically Bounded Concept Planning**: The GAT specialist operates on a concept graph. It predicts a deterministic transition path of concept nodes (e.g. `["List Input", "Modulo Condition", "List Comprehension", "Filtered Output"]`) that strictly respects GNN edge transition masks.
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| 4. **Autonomous Agent Code Generator**: The planned path context is passed to the local generative model (Ollama `qwen2.5-coder:3b`) to draft the source code.
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| 5. **Sandboxed Subprocess Executor**: Code runs inside a safe environment. Supported runtimes include **Python, JavaScript, C++, Go, SQL (SQLite3), HTML/CSS, Java, and Rust**.
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| 6. **Iterative Debugger**: If a run fails (non-zero exit code), the sandbox captures `stderr` and feeds it back to the agent for self-correction (up to 5 attempts).
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|
|
| ---
|
|
|
| ## 📊 Research Benchmarks & Scalability Results
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|
|
| The CAT V3/VLCM concept-based framework achieves massive memory compression and inference efficiency compared to standard token-based autoregressive models.
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|
|
| ### 1. Empirical Model Comparison
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| Benchmarked on the physical query: *"Why does compressor pressure ratio affect turbine efficiency?"*
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|
|
| | Metric | CAT V3 (Concept Graph-MoE) | Traditional Causal LLM (GPT-style) | Advantage / Scale Factor |
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| | :--- | :---: | :---: | :---: |
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| | **Model Parameters** | 2,294,835 | 721,900 | ~3.18x parameters |
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| | **Inference Latency** | **324.49 ms** | 232.31 ms | Linear execution / Single-pass |
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| | **Logic Hallucination Rate** | **0.0%** (Topologically Masked) | High (Unconstrained next-token drift) | **0% Hallucinations** |
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| | **Explainable Reasoning Trace**| **Yes** (100% Auditable Path) | No (Black-box attention states) | Full Audit Trail |
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|
|
| ### 2. CAT V3 Scalability Stress Test (100 ➔ 10,000 Concepts)
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| Demonstrating how the Graph-MoE routing and expert networks scale as the vocabulary size grows:
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|
|
| | Vocabulary Size | Avg Expert Activations | Inference Latency | RAM Footprint Increase | VRAM Usage |
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| | :---: | :---: | :---: | :---: | :---: |
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| | **100 Concepts** | 5.0 experts | 167.82 ms | +2.76 MB | 2.38 MB |
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| | **1,000 Concepts** | 3.7 experts | 232.51 ms | +3.82 MB | 10.77 MB |
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| | **10,000 Concepts** | 3.8 experts | 292.42 ms | -728.45 MB (cleanups) | 697.07 MB |
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|
|
| *Scaling the vocabulary by **100x** only increases latency by **1.7x** due to sparse routing, enabling massive scale-up on consumer CPUs.*
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|
|
| ### 3. VLCM Memory Footprint Savings (KV Cache vs. Graph State)
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| Comparison representing 100,000 tokens of corpus knowledge:
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| - **Sequence unit count**: 100,000 (LLM) vs. **5,000** (VLCM)
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| - **KV Cache size (Llama-3 70B at 8k context)**: **2.50 GB** vs. **131 KB** (VLCM Tiny Decoder)
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| - **Graph state memory**: **2.61 MB** (VLCM) ➔ **19,134.6x memory compression**
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| - **Generation FLOPs per query**: ~8.19 Trillion FLOPs vs. **~7.66 Million FLOPs** (1,000,000x savings)
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|
|
| ### 4. End-to-End Stress Test: 100,000 Concepts & Actual Code Generation
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|
|
| We stress-tested the performance, memory footprint, and reliability of the scaled symbolic reasoning engine using a **100,001-node coding concept graph with 1.2 Million directed edges**, paired with the local **Qwen 2.5 Coder 3B** model (`qwen2.5-coder:3b`) and a multi-language subprocess execution sandbox.
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|
|
| #### 📈 Stress Test Performance & Memory Metrics:
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| * **Graph Sizing**: **100,001 nodes** and **1,200,000 directed edges**
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| * **Graph Load Time**: **14.58 seconds** (deserializing and building the in-memory graph structure)
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| * **RAM Memory Footprint**: **1,501.59 MB** (approx. 1.50 GB)
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| * **Symbolic Traversal Latency (5-hop Beam Search)**: **121.81 ms** (average over 50 runs, highly optimized via pre-calculated activation mappings)
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| * **Average Code Generation Time**: **8.94 seconds** per task (System 1 inference)
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| * **Sandbox Code Execution Time**: **0.41 seconds** (System 2 sandbox execution)
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| * **Sandbox Compilation/Execution Success Rate**: **100.0%** (5 out of 5 tasks successfully compiled and passed on the first attempt)
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|
|
| ---
|
|
|
| #### 💻 Multi-Language Code Generation & Sandbox Results
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|
|
| We ran 5 distinct coding tasks across Python and JavaScript, enforcing strict concept planning paths to test compliance, syntax validity, and execution outcomes.
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|
|
| ````carousel
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| ### 🐍 Task 1: Fibonacci Sequence (Python)
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| * **Prompt**: *"Write a Python function fibonacci(n) that returns the first n Fibonacci numbers. In the main block, call this function with n=10, print the result, and do not use any interactive input() calls."*
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| * **GNN Concept Path**: `Array Allocation in Python` ➔ `Array Execution in Python` ➔ `Array Optimization in Python`
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| * **Generation Time**: 10.99 seconds
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| * **Sandbox Output**: `The first 10 Fibonacci numbers are: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]`
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|
|
| ```python
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| # Fibonacci sequence generator in Python
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|
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| def fibonacci(n):
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| '''
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| Generate the first n Fibonacci numbers.
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|
|
| Parameters:
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| n (int): The number of Fibonacci numbers to generate.
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|
|
| Returns:
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| list: A list containing the first n Fibonacci numbers.
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| '''
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| if n <= 0:
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| return []
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| elif n == 1:
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| return [0]
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|
|
| fib_sequence = [0, 1]
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| for i in range(2, n):
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| next_fib = fib_sequence[i-1] + fib_sequence[i-2]
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| fib_sequence.append(next_fib)
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|
|
| return fib_sequence
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|
|
| # Main block to test the fibonacci function
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| if __name__ == "__main__":
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| n = 10
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| try:
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| result = fibonacci(n)
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| print(f"The first {n} Fibonacci numbers are: {result}")
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| except Exception as e:
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| print(f"An error occurred: {e}")
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| ```
|
| <!-- slide -->
|
| ### 🐍 Task 2: Greatest Common Divisor (Python)
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| * **Prompt**: *"Write a Python function calculate_gcd(a, b) that computes the greatest common divisor using the Euclidean algorithm. Test it with inputs a=48 and b=18, print the result, and do not use interactive input() calls."*
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| * **GNN Concept Path**: `Variable Normalization in Python` ➔ `Variable Execution in Python` ➔ `Variable Optimization in Python`
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| * **Generation Time**: 6.26 seconds
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| * **Sandbox Output**: `The greatest common divisor of 48 and 18 is: 6`
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|
|
| ```python
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| # Function to calculate the greatest common divisor using the Euclidean algorithm
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| def calculate_gcd(a, b):
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| # Ensure both numbers are positive
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| a = abs(a)
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| b = abs(b)
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|
|
| # Use the Euclidean algorithm to find GCD
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| while b:
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| a, b = b, a % b
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|
|
| return a
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|
|
| # Main block with test data
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| if __name__ == "__main__":
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| a = 48
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| b = 18
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| try:
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| result = calculate_gcd(a, b)
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| print(f"The greatest common divisor of {a} and {b} is: {result}")
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| except Exception as e:
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| print(f"An error occurred: {e}")
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| ```
|
| <!-- slide -->
|
| ### 🐍 Task 3: Matrix Transpose (Python)
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| * **Prompt**: *"Write a Python function transpose(matrix) that transposes a 2D list (matrix) of size 3x3. Test it with matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]], print the transposed matrix, and do not use interactive input() calls."*
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| * **GNN Concept Path**: `Matrix Normalization in Python` ➔ `Matrix Execution in Python` ➔ `Matrix Optimization in Python`
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| * **Generation Time**: 8.75 seconds
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| * **Sandbox Output**:
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| ```text
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| Original Matrix:
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| [1, 2, 3]
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| [4, 5, 6]
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| [7, 8, 9]
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|
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| Transposed Matrix:
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| [1, 4, 7]
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| [2, 5, 8]
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| [3, 6, 9]
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| ```
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|
|
| ```python
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| import numpy as np
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|
|
| def transpose(matrix):
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| try:
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| if len(matrix) != 3 or any(len(row) != 3 for row in matrix):
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| raise ValueError("Input must be a 3x3 matrix.")
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|
|
| # Transpose using numpy's transpose function
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| transposed_matrix = np.array(matrix).T.tolist()
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| return transposed_matrix
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| except Exception as e:
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| print(f"An error occurred: {e}")
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| return None
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|
|
| if __name__ == "__main__":
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| matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
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| transposed_matrix = transpose(matrix)
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| if transposed_matrix is not None:
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| print("Original Matrix:")
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| for row in matrix:
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| print(row)
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| print("
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| Transposed Matrix:")
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| for row in transposed_matrix:
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| print(row)
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| ```
|
| <!-- slide -->
|
| ### 🟨 Task 4: Run-length Encoding (JavaScript)
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| * **Prompt**: *"Write a JavaScript function rle(input) that performs run-length encoding on a string. For example, 'AABBBCCCC' should return 'A2B3C4'. Call it with 'AABBBCCCC' in the main block and print the result using console.log. Do not use interactive prompt() calls."*
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| * **GNN Concept Path**: `String Parsing in JavaScript` ➔ `String Execution in JavaScript` ➔ `String Validation in JavaScript`
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| * **Generation Time**: 9.93 seconds
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| * **Sandbox Output**: `A2B3C4`
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|
|
| ```javascript
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| // Function to perform run-length encoding on a string
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| function rle(input) {
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| if (typeof input !== 'string') {
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| throw new Error('Input must be a string');
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| }
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|
|
| let encoded = '';
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| let count = 1;
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|
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| for (let i = 1; i < input.length; i++) {
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| if (input[i] === input[i - 1]) {
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| count++;
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| } else {
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| encoded += input[i - 1] + count;
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| count = 1;
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| }
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| }
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|
|
| // Add the last character and its count
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| encoded += input[input.length - 1] + count;
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| return encoded;
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| }
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|
|
| // Main block to test the rle function
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| try {
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| const inputString = 'AABBBCCCC';
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| const result = rle(inputString);
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| console.log(result); // Output: A2B3C4
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| } catch (error) {
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| console.error('Error:', error.message);
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| }
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| ```
|
| <!-- slide -->
|
| ### 🐍 Task 5: Bubble Sort (Python)
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| * **Prompt**: *"Write a Python function bubble_sort(arr) that sorts an array of integers in ascending order. Test it with inputs arr=[64, 34, 25, 12, 22, 11, 90], print the sorted array, and do not use interactive input() calls."*
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| * **GNN Concept Path**: `Array Optimization in Python` ➔ `Array Parsing in Python` ➔ `Array Execution in Python`
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| * **Generation Time**: 8.75 seconds
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| * **Sandbox Output**:
|
| ```text
|
| Original array: [64, 34, 25, 12, 22, 11, 90]
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| Sorted array: [11, 12, 22, 25, 34, 64, 90]
|
| ```
|
|
|
| ```python
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| # Bubble Sort Function in Python
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|
|
| def bubble_sort(arr):
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| n = len(arr)
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| for i in range(n):
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| for j in range(0, n-i-1):
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| if arr[j] > arr[j+1]:
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| arr[j], arr[j+1] = arr[j+1], arr[j]
|
|
|
| if __name__ == "__main__":
|
| arr = [64, 34, 25, 12, 22, 11, 90]
|
| try:
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| print("Original array:", arr)
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| bubble_sort(arr)
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| print("Sorted array:", arr)
|
| except Exception as e:
|
| print(f"An error occurred: {e}")
|
| ```
|
| ````
|
|
|
| ---
|
|
|
| ## 🚀 How to Run the Coding Lab locally
|
|
|
| 1. **Prerequisites**: Make sure you have python installed.
|
| 2. **Start the server**:
|
| ```bash
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| python coding_lab_server.py
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| ```
|
| 3. **Open the browser**: Navigate to **[http://localhost:8002/](http://localhost:8002/)**.
|
| 4. **Features**:
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| - Visual **Vis.js Concept Network** displaying active nodes and transition edges.
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| - Real-time **MoE routing probability bars**.
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| - Interactive tab panel showing the **Execution Trace logs**, **Generated Code**, and **Sandbox Stdout/Stderr**.
|
|
|
| ---
|
|
|
| ## 📂 Project Structure
|
|
|
| - `cat_v3/`: Core model definition, router, GAT experts, and combiner.
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| - `checkpoints/cat_v3/cat_v3_model.pt`: Pre-trained weights (Graph-MoE).
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| - `agent_executor.py`: Sandbox runner and execution manager.
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| - `coding_lab_server.py`: Web server hosting the GUI and APIs.
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| - `push_to_hf.py`: Helper script to synchronize files with Hugging Face Hub.
|
|
|
| ---
|
|
|
| ## ⚖️ License
|
| This project is licensed under the MIT License.
|
|
|
