醫學證據邏輯評測暴露缺口

Computer Science > Artificial Intelligence arXiv:2609.11185 (cs) [Submitted on 10 Sep 2026] Title:Can LLMs Follow Medical Expert Logic?

A Benchmark for Hierarchical Logical Consistency in Risk-of-Bias Assessment Authors:Jiayu Huang, Zichen Tang, Qianhui Ling, Zemin Kuang, Haihong E View a PDF of the paper titled Can LLMs Follow Medical Expert Logic?

A Benchmark for Hierarchical Logical Consistency in Risk-of-Bias Assessment, by Jiayu Huang and 4 other authors View PDF HTML (experimental) Abstract:Evidence-based medicine demands strict logical consistency, yet current evaluations of large language models (LLMs) prioritize superficial label matching over genuine reasoning.

We introduce LogiMed-RoB, a benchmark grounded in Cochrane Risk of Bias (RoB) 2.0 expert logic, comprising 860 randomized controlled trials (RCTs) and 14,820 queries.

It evaluates models under the Hierarchical Logical Consistency (HLC) framework across four dimensions: Atomic Consistency, Domain Consistency, Aggregation Consistency, and Evidential Faithfulness.

Experiments on 10 state-of-the-art LLMs reveal a catastrophic Error Compounding Effect: despite the top model reaching 98.88% Atomic Consistency, its end-to-end consistency collapses to 45.13%, with several open-weight architectures plummeting to nearly 0%.

We further uncover a systematic evidence-reasoning gap: even when models retrieve high-quality evidence, they fail to deduce correct outcomes in 18.63-40.05% of cases, while Blind Guess Rates reach 48.28%.

LogiMed-RoB demonstrates that high outcome accuracy can conceal critical reasoning flaws, underscoring the necessity of white-box logical verification for clinical deployment.Comments: Accepted to EMNLP 2026 Main Conference Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.11185 [cs.

AI] (or arXiv:2609.11185v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.

11185 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jiayu Huang [view email] [v1] Thu, 10 Sep 2026 07:46:41 UTC (1,458 KB) Full-text links: Access Paper: View a PDF of the paper titled Can LLMs Follow Medical Expert Logic?

A Benchmark for Hierarchical Logical Consistency in Risk-of-Bias Assessment, by Jiayu Huang and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

AI < prev | next > new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...

Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.

ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?

) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.

AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?

) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.Which authors of this paper are endorsers?| Disable MathJax (What is MathJax?)