Automated interpretability and feature discovery in language models with agents

Arnau Marin-Llobet, Javier Ferrando

Keywords: Automated Interpretability, Feature Discovery, AI Agents, Language Models, Interpretability with Agents, Sparse Autoencoders

TL;DR: An AI Agent that enables automated mechanistic interpretability and feature discovery in language models.

Abstract: We introduce an autonomous multiagent framework for mechanistic interpretability that automates both explaining and finding internal features in large language models. The system runs two coupled loops: (1) explanation refinement, where an agent proposes competing hypotheses and iteratively tests them with targeted prompt controls and a multi-metric evaluation; and (2) feature discovery, where an agent generates prompt sets, constructs a k-nearest-neighbor graph in activation space, and retrieves candidate features using statistical separability and semantic coherence criteria. On Gemma-2 family models and in weight-sparse transformers, our agent improves over one-shot auto-interpretations, discovers language-specific and safety-relevant features, and produces auditable explanation traces, showing that agent-driven empirical loops yield sharper and more falsifiable explanations than one-shot labels.

Track: Regular Paper (9 pages)

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Submission Number: 179