arXiv AI September 30, 2026 By Bo Zhang, Yuchen Wang, Dongbai Li, Matthew Yu Heng Wong, Qingkai Zeng, Lijun Wang, Tien-Yin Wong, Peng Cui, Tianyu Liu

RareDx: Controlled Knowledge Integration and Graph-Grounded Policy Optimization for Rare-Disease Diagnosis

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI

Jul 2

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.

By Deyang Jiang, Haoran Wu, Ziyi Wang, Yiming Rong, Yunlong Zhao, Ye Jin, Bo Xu

llms rag agents reinforcement-learning benchmarks

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

Sep 10

OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?

arXiv:2609.08174v1 Announce Type: new Abstract: We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs a...

By Xiao Yu Cindy Zhang, Wyeth Wasserman, Jian Zhu

llms rag fine-tuning benchmarks

arXiv AI

Jun 16

LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis

arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.

By Minh-Ha Nguyen, Erica Gray, Chih-Ting Yang, Rizwan Hamid, Lingyao Li, Siyuan Ma, Thomas A. Cassini, Cathy Shyr

llms agents fine-tuning benchmarks

arXiv Computation and Language

Sep 3

Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

The paper proposes a behavior-based fusion model that combines large language models (LLMs) with ontology rankers to improve rare-disease diagnosis. By examining ranked lists, agreement, and ontology support, the model learns how much to rely on each system per case, achieving significant recall gains on Phenopacket Store and RAMEDIS benchmarks. Importantly, the fused diagnoses retain candidate-level ontology evidence for inspection.

By Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim, Zicheng Li, Xuanqi Peng, Fei Teng, Jiacong Mi, Honghan Wu

llms benchmarks

arXiv AI

Jul 28

RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning

arXiv:2607. 23290v1 Announce Type: new Abstract: Rare diseases collectively affect an estimated 3.

By Xi Chen, Hongru Zhou, Shiyu Feng, Hanyu Zhou, Huahui Yi, Rongsheng Wang, Tiancheng He, Kun Wang, Pingping Liu, Qiankun Li, Sicheng Lin, Huiying Ou, Xiaohong Zheng, Tianying Zang, Zhuohang Wu, Leheng Jiang, Kexin Cao, Wenhan Zhang, ChengYi Li, Zhiyang Wang, Songlin Li, Benyou Wang, Ningbei Yin, Shaoting Zhang, Weili Fu, Jian Li, Kang Li

llms agents

One email a morning, machine-written

Related stories

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

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

OntologyBench: Can Dense Retrieval Satisfy Structured Biomedical Constraints?

LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis

Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning