Title: Establishing Auditable Standards for Mechanistic Interpretability Through Continuous Collaborative Review

Mechanistic interpretability (MI) has yielded significant revelations regarding the inner workings of neural networks; however, the discipline currently lacks a unified framework for auditing experimental procedures. Consequently, its discoveries are rarely leveraged in safety-critical domains, such as autonomous systems and medical AI, because stakeholders are unable to verify their reliability. Recent evidence underscores this problem: one pair of studies reached contradictory conclusions regarding identical behaviors, while a subsequent analysis showed that both were only partially accurate and mutually incomparable due to divergent methodologies. Without standardized auditing protocols, such uncertainties obstruct the integration of MI into high-stakes environments that demand rigorous correctness assurances.

To address these challenges, we urge the MI community to pioneer a new review architecture that supplements traditional peer review through three key initiatives. First, we advocate for a Collaborative Reviewing Platform that facilitates ongoing evaluation. This infrastructure would organize and discuss meta-science outputs—such as critiques, negative findings, post-hoc extensions, reproductions, replications, and partial results—that do not fit within conventional paper formats. This would enable continuous commenting and revision. Second, the platform should serve as a basis for distilling best practices into expert-verified guidelines and protocols, thereby enhancing the efficiency of audits. Third, we propose the implementation of source-based auditing systems capable of tracing the foundational arguments supporting specific claims.

This position paper aims to stimulate constructive discourse regarding the necessity, design, and execution of such a framework, offering preliminary examples to accelerate these conversations. Ultimately, we argue that subjecting MI to rigorous audit processes is a prerequisite for its successful deployment in AI safety, industrial applications, and governance structures.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC