Quantifying Faithful Confidence Expression in Large Reasoning Models

Forum topic · 小凯 · 2026-06-04

Summary

This paper introduces a novel framework for systematically quantifying faithful calibration (FC) in large reasoning models (LRMs)—the alignment between a model's intrinsic confidence and its linguistically expressed confidence. Reliable uncertainty communication is critical to LLM trustworthiness, yet FC remains a persistent failure mode. The challenge is especially important for LRMs, whose extended chain-of-thought reasoning traces are often interpreted by users as evidence of deliberation, competence, and confidence. Existing paradigms for measuring FC do not generalize well to long chain-of-thought outputs, which lack clear step boundaries, exhibit inconsistent step structure, and encode complex conditional dependencies, complicating intrinsic confidence estimation. The framework proposed by the authors—Areeb Gani, Asal Meskin, Gabrielle Kaili-May Liu, and Arman Cohan—addresses these gaps. Full paper: arXiv 2606.03969.

Paper Overview

Field: NLP Authors: Areeb Gani, Asal Meskin, Gabrielle Kaili-May Liu, Arman Cohan Posted: 2026-06-02 arXiv: 2606.03969

Summary

Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)—the alignment between models' intrinsic and (linguistically) expressed confidence—is a persistent failure mode.

This challenge is key for large reasoning models (LRMs), whose extended reasoning traces are often interpreted by users as evidence of deliberation, competence, and confidence. Despite the importance of FC and the wide usage of LRMs, the extent to which LRMs can faithfully express their confidence remains poorly understood.

Moreover, the prevailing paradigm to measure FC does not generalize well to the long chain-of-thought outputs generated by LRMs. These outputs tend to:

Lack clear step boundaries
Involve inconsistent step structure
Encode complex conditional dependencies throughout the trace

These properties complicate the estimation of intrinsic confidence.

To address this challenge, the authors introduce a novel framework to systematically quantify the faithful calibration of LRMs.

Original Abstract (excerpt)

> Reliable uncertainty communication is critical to the trustworthiness of LLMs, yet faithful calibration (FC)--the alignment between models' intrinsic and (linguistically) expressed confidence--is a persistent failure mode. This challenge is key for large reasoning models (LRMs), whose extended reasoning traces are often interpreted by users as evidence of deliberation, competence, and confidence.

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Quantifying Faithful Confidence Expression in Large Reasoning Models

Summary

Paper Overview

Summary

Original Abstract (excerpt)

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