---
title: "Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.05015v1"
permalink: "https://feed.astralor.com/items/itm_d18785167f99ed01260e6411"
published: "2026-08-05T16:21:18.000Z"
score: 71
channel: "ai"
tags: ["LLM","Decision theory","Label-free evaluation","Regularization","Representation theorems"]
---

# Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems

## AI commentary

> What stands out is turning 'rationality' into computable regularization signals without relying on human preferences or external labels. For researchers in LLM alignment, evaluation, and AI safety, this offers a practical decision-theoretic path worth studying closely.

## At a glance

This paper proposes leveraging representation theorems from decision theory for label-free evaluation and regularization of large language models and other AI systems. The central insight is that axiom compliance can be checked from a model's own responses to synthetic choice problems, without external labels or human feedback, and the resulting penalties are readily computable. Three instantiations are discussed: probabilistic coherence via de Finetti's theorem, preference rationality via Afriat's theorem, and subjective expected utility via Echenique and Saito's theorem. Because the axioms are necessary and sufficient, a passing model cannot be rejected on rationality grounds by any further test on the same data. These penalties complement, rather than replace, existing evaluation and training signals.

## Key points

- Applies representation theorems from decision theory to label-free evaluation of AI systems, checking axiom compliance from models' own responses.
- Provides three computable penalty instances: probabilistic coherence, preference rationality, and subjective expected utility.
- Because the axioms are necessary and sufficient, a model that passes cannot be rejected on rationality grounds by any further test of the same data.
- The method complements rather than replaces existing evaluation and training signals.

## Full text

Representation theorems in decision theory establish that behavior satisfies certain axioms if and only if it can be rationalized by a well-defined objective. I argue that this ``if and only if'' structure provides a potentially useful foundation for label-free evaluation and regularization of LLMs and other AI systems. Axiom compliance can be checked from the model's own responses to synthetic choice problems, with no external labels or human feedback, and the penalties are readily computable. Because the axioms are necessary and sufficient, the resulting checks exhaust the implications of the relevant rationality standard for the elicited data: a model that passes cannot be rejected on rationality grounds by any further test of the same data. I discuss three instantiations: probabilistic coherence via a theorem of de Finetti, preference rationality via Afriat's theorem, and subjective expected utility via a theorem of Echenique and Saito (2015), each yielding a continuous penalty that is zero whenever behavior can be rationalized. Since coherence does not restrict which objective rationalizes behavior, these penalties complement rather than replace other evaluation and training signals.

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