A multi-agent framework automates mechanistic interpretability in LLMs through coupled loops of hypothesis testing via prompts and feature discovery via activation-space graphs and statistical criteria.
The prompt includes the hypothesis text and up to 10 representative activating examples (truncated to 200 characters each)
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Automated Interpretability and Feature Discovery in Language Models with Agents
A multi-agent framework automates mechanistic interpretability in LLMs through coupled loops of hypothesis testing via prompts and feature discovery via activation-space graphs and statistical criteria.