Right Diagnoses, Decorative Reasoning:A Perturbation Audit of Medical Chain-of-Thought

2026-08-25

AI Summary

研究通过临床扰动审计方法，测试医学链式思维的忠实性，发现大多数模型中链式思维与答案脱节，即使修改链式内容也不影响答案准确性。

Abstract

Clinicians read chain-of-thought (CoT) rationales as evidence of medical reasoning, but whether the visible chain plays that role is rarely tested. General-domain CoT-faithfulness probes ignore clinical cost, and medical LLM evaluations treat the chain as a black box. We close this gap with a medical perturbation audit: a 30-operator battery edits both the chain and the question with clinically motivated operators (severity reversal, negation flip, demographic swap, evidence ablation), paired with a chain-update times answer-flip joint analysis that classifies each model by its failure mode. Applied to 14 LLMs on four medical QA benchmarks, three independent tests converge: the Chain-Decoupling Rate (CDR; chain does not register the edit and the answer does not flip) is 72.9% panel-wide on clinically meaningful destructive edits, chain corruption leaves accuracy unchanged, and removing CoT prompting does not reduce accuracy. Two board-certified clinicians re-annotate N=197 perturbed questions; 98.5% leave the gold defensible. The pattern holds across medical and reasoning fine-tuning and scale; on the closed-source tier, where the chain text is unavailable, the answer-side signals are consistent with the same decoupling. Our framework and CDR provide a reusable yardstick for auditing whether medical CoT is faithful or merely documentation.

Problem

• Medical Chain-of-Thought
• Faithfulness
• Clinical Reasoning

Innovation

• Perturbation Audit
• Chain-of-Thought
• Clinical Reasoning
• Chain-Decoupling Rate (CDR)