LLM-Trace

Systems-Oriented Interpretability & Inspection for Language Models

LLM-Trace is a tooling and interpretability library designed to treat language models as observable systems. It exposes the internal computation paths of transformer-based models, focusing on attention-based circuits and token-level influence.

Unlike standard evaluation tools that analyze inputs and outputs, LLM-Trace intercepts intermediate activations to explain how a model reaches a conclusion, bridging the gap between academic mechanistic interpretability and engineering diagnostics.

---

📖 Table of Contents

• Project Philosophy
• Core Features
• System Architecture
• Scope & Non-Goals
• Installation
• Roadmap
• Contributing
• License

---

💡 Project Philosophy

LLM-Trace is built on the premise that modern language models require observability, not just evaluation. The project adheres to three core tenets:

1. Measurement > Aesthetics: While visualization is supported, the primary goal is quantifiable metrics of influence (e.g., attention scores, activation magnitudes).
2. Causality > Correlation: We aim to validate findings through ablation and counterfactual testing, ensuring that detected circuits actually drive model behavior.
3. Systems Thinking: The model is treated as a computational graph where specific nodes (heads, layers) perform distinct sub-tasks.

---

🚀 Core Features

1. Internal Signal Extraction

• Attention Hooking: Direct extraction of attention matrices ($A$), Queries ($Q$), and Keys ($K$) from intermediate transformer layers without altering model weights.
• Activation Tracing: Capture residual stream states at critical junctures.

2. Circuit Detection & Analysis

• Token Influence Mapping: Identify which source tokens (e.g., a specific instruction) contribute most heavily to a target token's generation.
• Dependency Graphs: Visualize recurring patterns of information flow across layers.

3. Verification Utilities

• Ablation Testing: Tools to mask or zero-out specific attention heads to verify their contribution to the output.
• Prompt-Set Evaluation: Batch processing capabilities to test if a circuit holds true across varied inputs.

4. Inspection Console (Frontend)

• A dedicated React-based dashboard for navigating token-to-token relationships, heatmaps, and layer-wise comparisons.

---

🏗 System Architecture

LLM-Trace operates via a split architecture designed for performance and rigor:

• The Backend (Python/PyTorch): Handles model loading, hook injection, tensor manipulation, and metric calculation. It produces structured JSON/Tensor data representing the model's internal state.
• The Frontend (React/Node.js): Consumes structured data to render high-fidelity inspection interfaces. It acts as the "DevTools" for the model.

Conceptual Model

1. Input: A prompt is fed into a Transformer model.
2. Interception: Custom hooks capture activations during the forward pass.
3. Analysis: Raw tensors are processed into influence metrics (Intervention).
4. Output: Structured trace data is returned for visualization or programmatic analysis.

---

🎯 Scope & Non-Goals

In Scope

• Internal behavior inspection during inference.
• Mechanistic interpretability workflows.
• Safety audits via internal state monitoring.
• Small-to-Medium sized Transformer models (e.g., GPT-2, TinyLlama).

Out of Scope

• Training/Fine-tuning: This is an inference-only tool.
• Black-box Evaluation: We do not provide benchmarks like MMLU.
• Prompt Engineering: We analyze prompts, we do not optimize them.

---

⚙️ Installation

Prerequisites

• Python 3.10+
• Node.js 18+ (for Frontend dashboard)
• PyTorch (CUDA/MPS recommended)

Setup

# Clone the repository
git clone https://github.com/your-org/LLM-Trace.git
cd LLM-Trace

# Install Backend Dependencies
pip install -r requirements.txt

---

🗺 Roadmap

Phase 1 (Current): Core tracing logic for attention mechanisms. Basic heatmap visualization.

Phase 2: Implementation of ablation/intervention tools to prove causality.

Phase 3: MLP layer analysis and support for larger architectures (e.g., Llama-2 7B).

Phase 4: Automated "Circuit Discovery" pipelines.

---

🤝 Contributing

We welcome contributions from researchers and engineers. However, because this is a scientific tool, strict standards apply regarding code correctness, testing, and commit hygiene. Please read Contribution Guide before submitting a Pull Request.

Key Contribution Areas:

Core tracing logic & PyTorch hooks.

Influence metrics & statistical analysis.

Frontend data visualization (WebGL/Canvas).

Documentation & Reproducible Notebooks.

---

📄 License

• This project is licensed under the MIT License. See the LICENSE file for details. LLM-Trace is an open-source initiative to demystify machine intelligence.