Right Diagnoses, Decorative Reasoning: A Perturbation Audit of Medical Chain-of-Thought
Score
78首次提供医疗CoT忠实性的系统性扰动证据，打破思维链即推理的默认假设，信息增量显著。
实验设计严谨、三组独立测试相互印证，且有临床专家复核，证据链条完整。
CDR可作为评估医疗模型推理可靠性的可复用指标，对模型部署与合规审计有实用参考。
涉及当前最受关注的大模型可解释性与医疗落地信任问题，时效性强。
来自arXiv预印本，虽未经同行评审但团队背景与数据规模可信，结论具有启发性。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper is methodologically rigorous with strong converging evidence across models and benchmarks, directly contributing to medical AI evaluation and CoT interpretability research.
AI commentary
「What makes this paper worth reading is how it uses simple yet rigorous experiments to puncture an industry illusion: model-generated rationales may be beautiful documentation, not real reasoning. For medical AI product managers, clinical decision support developers, and regulatory auditors, CDR is a metric worth adopting for routine fidelity checks.」
Quick overview
Clinicians often read chain-of-thought (CoT) rationales as evidence of medical reasoning, but this paper tests whether that assumption holds. Using a 30-operator perturbation battery that edits both the chain and the question with clinically motivated operators—severity reversal, negation flip, demographic swap, and evidence ablation—the authors audit 14 large language models across four medical QA benchmarks. Three independent tests converge: the Chain-Decoupling Rate (CDR), measuring cases where the chain does not register the edit and the answer does not flip, is 72.9% on clinically meaningful destructive edits; chain corruption leaves accuracy unchanged; and removing CoT prompting does not reduce accuracy. Two board-certified clinicians re-annotated 197 perturbed questions, finding 98.5% of gold answers defensible. The pattern persists across medical and reasoning fine-tuning and scale; closed-source models show answer-side signals consistent with the same decoupling. The framework and CDR offer a reusable yardstick for auditing whether medical CoT is faithful or merely documentation.
- Introduces a 30-operator perturbation battery editing both chain and question, paired with a chain-update times answer-flip joint analysis.
- Across 14 LLMs and 4 medical QA benchmarks, average CDR is 72.9%; chain corruption leaves accuracy unchanged and removing CoT does not reduce performance.
- Two board-certified clinicians re-annotated 197 perturbed questions; 98.5% of gold answers remain defensible.
- Suggests medical CoT may be decorative rather than faithful; CDR offers a reusable yardstick for faithfulness auditing.
Full text
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