Pitfalls in Evaluating Interpretability Agents

arXiv·medium signal

Automated interpretability systems—which aim to explain neural network behaviors at scale—have subtle evaluation flaws that inflate reported performance metrics, according to this systematic analysis. The paper identifies key failure modes including circular evaluation loops and distribution mismatch between training and test concepts. Critical reading for anyone building or benchmarking automated interpretability tooling: many published numbers are overstated.