Paper argues mechanistic interpretability needs a logic of inference
Original title: Mechanistic Interpretability Needs a Logic of Inference
The authors propose five principles as a recursive procedure for turning local findings into testable mechanistic claims.
The paper says mechanistic interpretability can identify neurons, features, circuits, and dynamics, but still lacks a rigorous way to infer mechanisms from those observations. It compares recurring inferential bottlenecks in neuroscience and interpretability. The authors organize five principles into a recursive logic meant to turn local findings into cumulative knowledge and hypotheses beyond the model. They state that evidence outside interpretability is still required to say what those mechanisms reveal about the world.
- The focus is inference from observations, not new circuit-finding tools.
- Five principles are proposed as a recursive inference procedure.
- Authors say external evidence is required to link model mechanisms to the world.
From the original article
1 min read at arxiv.orgNeural networks are increasingly studied not merely as predictive systems, but as complex systems whose internal computations are themselves objects of scientific inquiry. Recent progress in mechanistic interpretability (MI) has made it possible to identify neurons, features, circuits, population structures, and dynamical patterns associated with model behavior.
By Zhimin Hu · arxiv.org
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