Scale thorough mechanistic analysis of model computations

Develop scalable methodologies for thorough mechanistic analysis of the computations inside modern neural networks so that auditors can obtain strong, general assurances about model behavior across broad classes of inputs, extending beyond small or simple tasks.

Background

The authors argue that mechanistic understanding of internal computations can provide stronger assurances than black-box testing alone, because it enables predictive reasoning over classes of inputs rather than individual examples. They note prior work achieving detailed analyses on simple tasks but emphasize that scaling this level of understanding remains unresolved.

This scaling challenge is central to using interpretability for audits: without scalable, thorough analysis, auditors cannot reliably translate internal insights into broad behavioral guarantees for large, complex models.

References

Although scaling thorough analysis is an open challenge, it offers a strategy for making strong assurances.

— Black-Box Access is Insufficient for Rigorous AI Audits  (2401.14446 - Casper et al., 2024) in Section 4.2, White-box interpretability tools aid in diagnostics

Mechanistic interpretability seeks to reverse-engineer neural networks and identify the algorithms, features and concepts represented internally. But interpreting networks with billions of parameters remains an unsolved problem.

— The Past and Future of AI Scientists  (2608.14407 - King, 14 Aug 2026) in Section 5.4.6, page 31

A related limitation is that our circuits are sets of units engaged by the problems, and we do not directly link them to the algorithms they implement. In other words, we can measure whether two formats engage the same units, but we cannot infer the computations that those units support. Connecting the implementation level (which components a model uses) to the algorithmic level (what procedure those components carry out; \citealp{marr1982}) would be necessary to move from statistical prediction, which is what we have now, to formal guarantees of correctness, which is the ultimate requirement for deploying LLMs in high-stakes settings.

— Shared circuits predict whether LLMs generalize across formats in arithmetic reasoning  (2609.04463 - Varda et al., 3 Sep 2026) in Section “Limitations”