Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
Ayan Antik Khan, Harsh Kohli, Yuekun Yao, Huan Sun, Ziyu Yao
Why It Matters
What makes this one worth your time
Understanding how components in neural networks function is crucial for advancing interpretability, which can lead to more reliable AI systems.
Language model agents show promise in explaining transformer circuit components.
Summary
The paper investigates the use of language model agents to assist in explaining the functionality of identified components within transformer circuits, introducing a benchmark and a methodology called HyVE for generating explanations.
Key contributions
- Introduction of AgenticInterpBench, a benchmark for circuit explanation.
- Development of the HyVE methodology for generating component-level explanations.
- Empirical analysis of the performance of various LM backbones in the context of circuit explanation.
Notable insights
- The iterative loop of observation, hypothesis generation, and causal validation in HyVE is a novel approach to circuit explanation.
- The performance variability across different LM backbones highlights the complexity of the validation process in mechanistic interpretability.
Possible limitations
- The abstract does not address the scalability of the proposed methods to larger or more complex circuits.
- Potential issues with the reliability of validation processes are noted but not fully explored.
Abstract
arXiv:2606.24026v1 Announce Type: new 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.