Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
Quick Answer
This study introduces AgenticInterpBench, a benchmark for circuit explanation using LM agents like HyVE, which generates component-level explanations through iterative observation and validation.
Quick Take
Results show varying performance across four LM backbones, highlighting the potential of LM agents in mechanistic interpretability, though reliable validation remains a challenge.
Key Points
- AgenticInterpBench consists of 84 semi-synthetic transformer circuits with 163 annotations.
- HyVE employs an iterative process of observation, hypothesis generation, and causal validation.
- No single LM backbone consistently outperforms others in generating explanations.
- Strong backbones typically create observation-grounded hypotheses, but validation issues persist.
- A case study on Llama-3-8B demonstrates applicability beyond semi-synthetic benchmarks.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | 23 pages, 4 figures, 14 tables |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.24026 [cs.AI] |
| (or arXiv:2606.24026v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24026 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ayan Antik Khan [view email]
[v1]
Tue, 23 Jun 2026 00:04:31 UTC (239 KB)
— Originally published at arxiv.org
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