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The paper presents a simple pipeline that uses LLMs to automatically group features into supernodes in attribution graphs, matching human annotator interpretability and recovering intermediate hop supernodes in a two-hop task.
circuit-tracer is a Python library that helps interpretability researchers map model computations by calculating direct effects between transcoder features and output logits, then visualizing them as an interactive attribution graph.
An article summarizing Anthropic's 2025 paper on mechanistic interpretability, showing that LLMs are not black boxes and that circuit tracing can reveal multi-step reasoning and human-identifiable concepts.
This paper investigates why Retrieval-Augmented Generation (RAG) systems fail despite having access to correct evidence. Using circuit tracing and attribution graphs, the authors find that correct predictions exhibit deeper reasoning paths and more distributed evidence flow, while failures show shallow and fragmented patterns. They propose a graph-based error detection framework and targeted interventions to improve RAG reliability.