Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?

• URL: http://arxiv.org/abs/2606.24026v1
• Date: Tue, 23 Jun 2026 00:04:31 GMT
• Title: Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
• Abstract summary: We introduce AgenticInterpBench, a benchmark for circuit explanation built from 84 semi-synthetic transformer circuits.<n>We propose HyVE (Hypothesize,, Explain), an agentic explainer that analyzes each component through an iterative loop of observation, hypothesis generation, and causal validation.<n>Across four LM backbones, HyVE recovers useful component- and task-level explanations, but no backbone is uniformly best.
• Score: 17.61177889205537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mechanistic interpretability has made substantial progress in automatically localizing circuits, but explaining what localized components do remains labor-intensive and difficult to standardize. In this work, we study whether language model (LM) agents can assist with this explanation problem once a circuit has already been identified. We introduce AgenticInterpBench, a benchmark for circuit explanation built from 84 semi-synthetic transformer circuits with 163 component-level annotations. We propose HyVE (Hypothesize, Validate, Explain), an agentic explainer that analyzes each component through an iterative loop of observation, hypothesis generation, and causal validation, eventually producing a component-level explanation and a circuit-level task description. Across four LM backbones, HyVE recovers useful component- and task-level explanations, but no backbone is uniformly best. Our analysis shows that strong backbones usually form observation-grounded hypotheses, while failures more often arise later in the validation loop, through incomplete validation plans, code execution errors, or unresolved hypotheses. A case study on an arithmetic circuit in Llama-3-8B shows that the same formulation can extend beyond semi-synthetic benchmarks to naturally trained models. Overall, LM agents are promising circuit explainers, but reliable validation remains the key obstacle.

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