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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.
This paper investigates how toxic lexical perturbations in prompts reduce the factual accuracy and increase uncertainty of LLMs, and uses attribution-graph analyses to trace internal changes. It finds that increasing toxicity amplifies perturbation-sensitive variant nodes while core reasoning nodes remain invariant.
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.