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 agen
The rapid advancement and increased complexity of large language models necessitate automated and standardized methods for mechanistic interpretability to ensure safety and reliability.
Improving the interpretability of AI circuits is critical for building trustworthy and controllable AI systems, particularly as AI agents become more autonomous and influential.
This research introduces a benchmark and methodology for enabling language model agents to explain AI circuit behavior, moving toward more automated and scalable interpretability.
- · AI safety researchers
- · AI developers
- · AI auditing firms
- · Mechanistic interpretability field
- · Manual interpretability methods
- · Black box AI systems
Automated explanations accelerate the identification and remediation of undesirable AI behaviors.
Increased transparency in AI models fosters greater public trust and facilitates broader deployment of advanced AI applications.
The ability to rapidly understand and modify complex AI systems could lead to exponential acceleration in AI development and capability scaling.
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Read at arXiv cs.AI