arXiv:2510.00845v4 Announce Type: replace Abstract: Mechanistic Interpretability (MI) aims to reverse-engineer model behaviors by identifying functional sub-networks. Yet, the scientific validity of these findings depends on their stability. In this work, we argue that circuit discovery is not a standalone task but a statistical estimation problem built upon causal mediation analysis (CMA). We uncover a fundamental instability at this base layer: exact, single-input CMA scores exhibit high intrinsic variance, implying that the causal effect of a component is a volatile random variable rather t
The increasing complexity and scale of AI models necessitate robust methods for understanding their internal workings, making mechanistic interpretability a critical and evolving field.
This research highlights a fundamental instability in a core method of AI interpretability, suggesting that current approaches might be less reliable than assumed and require significant refinement for trustworthy AI.
The perceived reliability and scientific validity of mechanistic interpretability findings are now formally challenged, pushing the field towards more statistically rigorous and stable methods.
- · AI safety researchers
- · Statisticians and causal inference experts
- · Developers of new interpretability techniques
- · Practitioners relying solely on exact single-input CMA scores
- · AI fields lacking robust interpretability validation
Increased research focus on developing stable and statistically sound mechanistic interpretability methods.
Potential delays or increased scrutiny for AI systems whose safety or reliability claims heavily depend on current, unstable interpretability techniques.
Long-term, more trustworthy and explainable AI systems, but with a temporary slowdown in certain interpretability-dependent applications.
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Read at arXiv cs.LG