Right Diagnoses, Decorative Reasoning:A Perturbation Audit of Medical Chain-of-Thought
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 7 of its 37 references that are in the map. First posted on arXiv 25 Aug 2026.
Builds on
7 of 37 references.
References · 7
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- 2023Mistral 7Bcited by 3,164
- 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
- 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
- 2023MEDITRON-70B: Scaling Medical Pretraining for Large Language Modelscited by 274
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Better Accuracies, Worse Reasoning: A Step-Level Audit of Medical Chain-of-Thought Distillation
- 2025Why Chain of Thought Fails in Clinical Text Understanding
- 2026Faithful or Just Plausible? Evaluating the Faithfulness of Closed-Source LLMs in Medical Reasoning
- 2026To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
- 2026Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering
- 2025Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
- 2026CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning
- 2026Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine