Mechanistic Interpretability of Tool Selection in LLMs

by Alex Baumgartner4 months ago
24

LLMs increasingly use tools (search, code execution, APIs), but we have no mechanistic understanding of how they decide which tool to call. Existing agent benchmarks treat selection as a black box, measuring success/failure without explaining the decision.

What representations drive tool choice? Is it semantic matching between tool descriptions and queries, or learned heuristics? Do models have dedicated "routing" circuits, or is selection distributed? Understanding this seems important as tool-using agents become more prevalent since selection errors cause cascading failures and we currently can't predict or debug them.

Mechanistic InterpretabilityAIToolsImplemented:https://github.com/Hypogenic-AI/mechanistic-llm-tools-claudeImplemented:https://github.com/Hypogenic-AI/llm-tool-selection-codexImplemented:https://github.com/Hypogenic-AI/mechanistic-llm-tools-gemini
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If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:

@misc{baumgartner-mechanistic-interpretability-of-2026,
  author = {Baumgartner, Alex},
  title = {Mechanistic Interpretability of Tool Selection in LLMs},
  year = {2026},
  url = {https://hypogenic.ai/ideahub/idea/w0WV6HB3ztAkjTZtxLcW}
}

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