Anthropic Circuit Tracing Tools
| Introduction: | Anthropic has open-sourced a library and interactive frontend for generating and exploring attribution graphs, which reveal the internal steps a large language model takes to produce an output. |
| Recorded in: | 6/5/2025 |
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What is Anthropic Circuit Tracing Tools?
Anthropic's Circuit Tracing Tools are an open-source initiative comprising a Python library and an interactive web frontend (hosted on Neuronpedia) designed to enhance the interpretability of large language models (LLMs). The core concept involves generating "attribution graphs" that partially reveal the internal computational steps an LLM takes to arrive at a specific output. This project aims to make it easier for the broader research community to understand the complex inner workings of AI models, addressing the critical need for interpretability in rapidly advancing AI capabilities. It is primarily targeted at AI researchers, developers, and anyone interested in delving into the "thoughts" or internal mechanisms of LLMs.
How to use Anthropic Circuit Tracing Tools
Users can begin by visiting the Neuronpedia interface to interactively generate and view attribution graphs for prompts of their choosing. For more advanced research and sophisticated usage, the underlying code repository is available for direct access and modification. The tools are open-source, implying no direct cost or specific registration requirements beyond standard GitHub usage for the library or web access for Neuronpedia. Key interactions involve generating graphs, visualizing them in an interactive frontend, annotating and sharing findings, and testing hypotheses by modifying feature values to observe changes in model outputs.
Anthropic Circuit Tracing Tools's core features
Generation of attribution graphs to trace internal model steps
Support for popular open-weights language models
Interactive visualization and exploration of graphs via Neuronpedia
Ability to annotate and share generated graphs
Tools for testing hypotheses by modifying feature values and observing output changes
Open-source library for community contribution and extension
Provision of demo notebooks with examples and analysis
Use cases of Anthropic Circuit Tracing Tools
Studying multi-step reasoning processes within large language models
Analyzing how multilingual representations are formed and used by LLMs
Debugging and understanding unexpected or undesirable behaviors in AI models
Identifying and mapping specific "circuits" or internal computational pathways
Advancing AI safety research by improving the transparency and interpretability of models
Facilitating educational purposes for researchers and students learning about LLM internals
Developing novel interpretability techniques and tools based on the open-sourced framework
Collaborating with the community to discover and analyze new interesting circuits