Towards Worst-Case Guarantees with Scale-Aware Interpretability

2026-02-05

AI Summary

Existing model interpretability methods struggle to explicitly model multiscale feature interactions and lack worst-case guarantees regarding fine-grained structural influences. This work proposes a scale-aware interpretability framework that, for the first time, systematically integrates renormalization group theory from statistical physics into AI interpretability. By combining multiscale feature analysis with formal verification, the framework constructs a rigorous tool capable of tracing cross-resolution feature interactions and providing provable bounds on their influence. This approach establishes a theoretically grounded and technically viable pathway toward robust and faithful interpretability, particularly critical for safety-sensitive AI applications.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Neural networks organize information according to the hierarchical, multi-scale structure of natural data. Methods to interpret model internals should be similarly scale-aware, explicitly tracking how features compose across resolutions and guaranteeing bounds on the influence of fine-grained structure that is discarded as irrelevant noise. We posit that the renormalisation framework from physics can meet this need by offering technical tools that can overcome limitations of current methods. Moreover, relevant work from adjacent fields has now matured to a point where scattered research threads can be synthesized into practical, theory-informed tools. To combine these threads in an AI safety context, we propose a unifying research agenda -- \emph{scale-aware interpretability} -- to develop formal machinery and interpretability tools that have robustness and faithfulness properties supported by statistical physics.

Problem

• scale-aware interpretability
• neural networks
• multi-scale structure
• worst-case guarantees
• feature composition

Innovation

• scale-aware interpretability
• renormalisation
• worst-case guarantees
• feature composition
• statistical physics