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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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 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

Jul 29

EviDAG: Auditable Causal DAG Authoring with Biomedical Literature

arXiv:2607. 21859v2 Announce Type: replace Abstract: Constructing causal directed acyclic graphs (DAGs) is a core step in biomedical causal analysis, yet it remains a largely manual process.

By Yi-han Sheu, Michael R. Steigman, Yu Zhou, Bo Wang, Fan-Yu Yen, Jordan W. Smoller

llms benchmarks

arXiv AI

Jun 11

Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

arXiv:2606. 11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap.

By Haoyang Zeng, Yuanxi Fu, Rongzhen Li, Yuming Yang, Xiao Sun, Jingwang Huang, Gujie Shao, Guohui Xiang, Quan Lu, Dongfan Ye, Xuetao Chen, Jiang Zhong, Kaiwen Wei, Zhi Xu

llms nlp reinforcement-learning benchmarks

arXiv AI

Jul 28

Structure Over Scale: Schema-Constrained Causal Graphs for RAG

arXiv:2607. 22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships exhaustively, producing graphs whose size and construction cost scale with corpus length rather than with the reasoning a query requires.

By Marc Saouda (Boston Consulting Group), Rajprakash Bale (Boston Consulting Group), Eren Aldis (Boston Consulting Group), Cloves Almeida (Boston Consulting Group)

llms rag benchmarks

arXiv AI

Aug 5

GENESIS: Towards Explainable Causal Discovery

arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.

By Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat, Harsh Vardhan Singh Chauhan, Niranjan Pedanekar

llms benchmarks

Related stories

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

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

EviDAG: Auditable Causal DAG Authoring with Biomedical Literature

Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning

Structure Over Scale: Schema-Constrained Causal Graphs for RAG

GENESIS: Towards Explainable Causal Discovery