@inproceedings{xu-etal-2026-right,
title = "Right Diagnoses, Decorative Reasoning: A Perturbation Audit of Medical Chain-of-Thought",
author = "Xu, Mengzhu and
Gao, Jifan and
Jiang, Xia and
Wu, Yaoxin and
Long, Xi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.334/",
pages = "7689--7707",
ISBN = "979-8-89176-459-0",
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 M2/M4/M5 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, scale, and the closed-source subset. Our framework and CDR provide a reusable yardstick for auditing whether medical CoT is faithful or merely documentation."
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<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 M2/M4/M5 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, scale, and the closed-source subset. Our framework and CDR provide a reusable yardstick for auditing whether medical CoT is faithful or merely documentation.</abstract>
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%0 Conference Proceedings
%T Right Diagnoses, Decorative Reasoning: A Perturbation Audit of Medical Chain-of-Thought
%A Xu, Mengzhu
%A Gao, Jifan
%A Jiang, Xia
%A Wu, Yaoxin
%A Long, Xi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xu-etal-2026-right
%X 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 M2/M4/M5 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, scale, and the closed-source subset. Our framework and CDR provide a reusable yardstick for auditing whether medical CoT is faithful or merely documentation.
%U https://aclanthology.org/2026.emnlp-main.334/
%P 7689-7707
Markdown (Informal)
[Right Diagnoses, Decorative Reasoning: A Perturbation Audit of Medical Chain-of-Thought](https://aclanthology.org/2026.emnlp-main.334/) (Xu et al., EMNLP 2026)
ACL