Right Diagnoses, Decorative Reasoning:A Perturbation Audit of Medical Chain-of-Thought
cs.AI
Submitted: 2026-08-25
Updated: 2026-08-25
License: http://creativecommons.org/licenses/by/4.0/
The gist: Clinicians read chain-of-thought (CoT) rationales as evidence of medical reasoning, but whether the visible chain plays that role is rarely tested.
Terminology
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.
Sources
- MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
- Training Verifiers to Solve Math Word Problems
- Gemma 2: Improving Open Language Models at a Practical Size
- The Llama 3 Herd of Models
- Mistral 7B
- Measuring Faithfulness in Chain-of-Thought Reasoning
- Qwen2.5 Technical Report
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