Principled and reproducible evaluation frameworks for localization and explanation methods

Develop principled and reproducible evaluation frameworks that rigorously compare localization methods and assess whether identified model components are causally optimal, enabling reliable benchmarking of mechanistic interpretability techniques.

Background

The authors highlight a lack of unified benchmarks and standardized evaluation protocols for localization, making it difficult to compare methods or verify that identified components are truly causal drivers of behavior. This also affects downstream applications that rely on single localization techniques without guarantees.

A robust evaluation framework would standardize faithfulness assessments and support reproducibility across tasks and models, improving the reliability of MI-informed interventions.

References

Some works partially mitigate this issue by combining multiple localization techniques and examining whether they converge on similar model components, but developing principled and reproducible evaluation frameworks remains an open challenge.

— Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models  (2601.14004 - Zhang et al., 20 Jan 2026) in Section “Limitation”