arXiv AI August 18, 2026 By Ummara Mumtaz, Aimen Noor, Awais Ahmed

Grounding Healthcare LLMs in a Causal Knowledge Graph: Framework, Metrics, and a Cardiovascular Pilot

arXiv:2608. 15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty.

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llms

arXiv AI

Aug 25

Framework for Grounding Healthcare LLMs in a Causal Knowledge Graph: A Cardiovascular Example Pilot

arXiv:2608.15382v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy r...

By Ummara Mumtaz, Aimen Noor, Awais Ahmed

llms

arXiv Machine Learning

Aug 5

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

arXiv:2608. 02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive models.

By Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes, Mahdi Bagheri, Pratik Gajane, Amir M. Rahmani

llms benchmarks safety

arXiv Computation and Language

Sep 22

Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering

arXiv:2606.22419v3 Announce Type: replace Abstract: A recent Nature Medicine study reports that general-purpose frontier LLMs outperform specialized retrieval-augmented clinical tools on medical benc...

By Madhulatha Mandarapu, Sandeep Kunkunuru

llms rag agents nlp benchmarks

arXiv AI

Jun 30

Clinical Reasoning Graphs: Structured Evaluation of LLM Diagnostic Reasoning Reveals Competence Without Consistency

arXiv:2606. 29876v1 Announce Type: cross Abstract: Modern large language models (LLMs) reach 60-70% diagnostic accuracy on complex clinical case benchmarks, but accuracy alone cannot distinguish stable clinically-grounded reasoning from pattern matching.

By Nisarg A. Patel (University of California, San Francisco)

llms benchmarks

arXiv AI

4d ago

A Proposed Rubric for Evaluating Expressed Clinical Reasoning in Large Language Model Responses

arXiv:2609.37788v1 Announce Type: cross Abstract: Rubrics support the structured evaluation of language models. We propose a rubric for assessing expressed clinical reasoning in model responses, draw...

By Zhangshu Joshua Jiang, Zina Ibrahim, James T. Teo

llms benchmarks safety

arXiv Computer Vision

Aug 24

Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement

arXiv:2608.10725v2 Announce Type: replace Abstract: Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing mo...

By Uma Ranjan, Kunal Tilaganji, Aditya Koul, Anurag Mahipal, Dashpreet Singh, Hriday Rana, Manan Jain, Sidharth Gupta, Ajo Babu George, Vineeth Balasubramanian, Nagarajan Natarajan, Amit Sharma

llms safety

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Related stories

Framework for Grounding Healthcare LLMs in a Causal Knowledge Graph: A Cardiovascular Example Pilot

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering

Clinical Reasoning Graphs: Structured Evaluation of LLM Diagnostic Reasoning Reveals Competence Without Consistency

A Proposed Rubric for Evaluating Expressed Clinical Reasoning in Large Language Model Responses

Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement