arXiv AI
Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning
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arXiv AI
Jul 28
MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models
arXiv:2607. 22566v1 Announce Type: new Abstract: MedLoCoMo is a Medical Long-Context Memory benchmark for patient-specific clinical reasoning over multi-admission medical dialogue.
arXiv AI
Jul 21
Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry
arXiv:2505. 02722v2 Announce Type: replace Abstract: Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limited.
arXiv AI
Jun 9
From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
arXiv:2603. 03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.
arXiv AI
Sep 1
EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning
arXiv:2601.03471v4 Announce Type: replace-cross Abstract: Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effe...
arXiv AI
2d ago
A rubric landscape for evaluating clinical reasoning in large language models: what exists, what is missing, and what needs to be combined
arXiv:2610.01938v1 Announce Type: cross Abstract: Exam-style accuracy does not establish whether large language models (LLMs) reason well over clinical records. We define clinical reasoning as integr...
arXiv AI
Sep 15
Hindsight Bias in Clinical Temporal Reasoning: How Future Data Exposure Affects Large Language Model Judgment
The paper introduces a paired benchmark to detect hindsight bias in clinical language models by comparing model responses to questions posed at a clinically relevant cutoff versus the full timeline. It uses 171 case reports (40 sepsis, 131 GLP‑1/diabetes) with both human‑annotated and LLM‑generated time‑series data, evaluating accuracy, hindsight trap rate, answer instability rate, and hindsight bias rate. Results show that exposing models to the full timeline consistently increases hindsight bias, while truncating the timeline mitigates bias without sacrificing accuracy.