mlsanityData to 7 Oct 2026

A Mathematical Theory of Interpretation: Rational Entropy, Spectral Readout, and Confusability as a Resource

arXiv 2608.23892 ↗First posted 24 Aug 2026cs.IT cs.AI math.ITcited by 0

Not on the map. Why?

Abstract (arXiv)

This article presents the abridged core of A Mathematical Theory of Interpretation (MTI), which treats interpretation as observer-relative spectral measurement under an access structure. MTI makes interpretation a method-design problem: access, query, utility, and medium determine what an observer can select, identify, communicate, or refuse. On a learning-invariant Hilbert realization, Rational Entropy measures residual uncertainty across knowledge, utility, and medium. In the finite-effective regime, we classify its zero set. Pairwise confusability is equivalent to uniform atomic collapse, while a unique utility maximum can select one atom even when other zero-cost states remain non-atomic. This reverses the usual zero-error role of confusability: agreement in at least one observer direction excludes unresolved multi-atom readings, while the joint label preserves identification. The corresponding free-design capacity is the product of all but the smallest direction budget. A four-condition certificate characterizes sharp, decodable, medium-faithful, and order-independent readout on a finite commuting code sector and returns typed obstructions when those guarantees fail. Together, these results establish MTI as a theoretical basis for constructing interpretation methods with explicit access assumptions, guarantees, and failure modes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 24 references that are in the map. First posted on arXiv 24 Aug 2026.

Builds on

5 of 24 references.

References · 5

  1. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  2. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  3. 2015An Introduction to Matrix Concentration Inequalitiescited by 391
  4. 2026Causality is Key for Interpretability Claims to Generalisecited by 8
  5. 2024Explainable AI needs formalizationcited by 4

Built on by

Not cited yet.