LLMs Can Annotate Attribution Graphs
arXiv:2608.02632v1 Announce Type: new Abstract: Circuit tracing is an exciting technique for revealing the internal computation of language models, but it requires a time-intensive manual step of grouping individual features or MLP neurons into supernodes. We present a simple pipeline for automating this step: directly presenting feature descriptions to a language model that groups them into supernodes. Using automated interpretability metrics, we confirm that supernodes generated by our pipeline are as interpretable as those generated by human annotators. On a two-hop Capitals task, our pipeline recovers a supernode corresponding to the intermediate hop in 97 of 100 prompts. Finally, we present a simple proof of concept using our pipeline for open-ended exploration, where we automatically annotate 1000 attribution graphs from Wikipedia prompt completions and then use an LLM judge to flag interesting graphs worth human review. We hope this work demonstrates that even simple automation can produce meaningful attribution graph annotations, motivating further work on automated circuit tracing.
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[Submitted on 28 Jul 2026]
Title:LLMs Can Annotate Attribution Graphs
View a PDF of the paper titled LLMs Can Annotate Attribution Graphs, by Ameen Patel and 2 other authors
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Abstract:Circuit tracing is an exciting technique for revealing the internal computation of language models, but it requires a time-intensive manual step of grouping individual features or MLP neurons into supernodes. We present a simple pipeline for automating this step: directly presenting feature descriptions to a language model that groups them into supernodes. Using automated interpretability metrics, we confirm that supernodes generated by our pipeline are as interpretable as those generated by human annotators. On a two-hop Capitals task, our pipeline recovers a supernode corresponding to the intermediate hop in 97 of 100 prompts. Finally, we present a simple proof of concept using our pipeline for open-ended exploration, where we automatically annotate 1000 attribution graphs from Wikipedia prompt completions and then use an LLM judge to flag interesting graphs worth human review. We hope this work demonstrates that even simple automation can produce meaningful attribution graph annotations, motivating further work on automated circuit tracing.
Comments: Poster at ICML Mechanistic Interpretability Workshop 2026
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2608.02632 [cs.LG]
(or arXiv:2608.02632v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.02632
arXiv-issued DOI via DataCite
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From: Ameen Patel [view email] [v1] Tue, 28 Jul 2026 04:12:08 UTC (5,355 KB)
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