Spectral Identifiability Proves Model Circuits Aren’t Just Method Artifacts
The authors prove that the Koopman spectrum of a neural net—a coordinate‑free, model‑intrinsic fingerprint—can be recovered from a finite number of activation samples, with provable error rates. This gives the first identifiability theorem for mechanistic interpretability, letting researchers separate genuine circuits from artifacts, and the method scales across GPT‑2, Gemma‑2‑2B, and Qwen‑8B.