- PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering
Yiqing Zhang, Xiaozhong Liu, Fabricio Murai · Mar 28, 2026 · Citations: 0
Expert Verification Llm As JudgeAutomatic Metrics
In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata)…
- Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning
Zhiwen You, Xi Chen, Aniket Vashishtha, Simo Du, Gabriel Erion-Barner · Mar 29, 2026 · Citations: 0
Expert Verification Human EvalAutomatic Metrics Multi Agent
In this work, we propose a counterfactual multi-agent diagnostic framework inspired by clinician training that makes hypothesis testing explicit and evidence-grounded.
- Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study
Yosuke Yamagishi, Atsushi Takamatsu, Yasunori Hamaguchi, Tomohiro Kikuchi, Shouhei Hanaoka · Apr 2, 2026 · Citations: 0
Pairwise Preference Llm As JudgeAutomatic Metrics
A board-certified radiologist and a radiology resident independently performed blinded pairwise evaluations across 4 criteria: terminology accuracy, readability, overall quality, and radiologist-style authenticity.
- Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
Xue Liu, Xin Ma, Yuxin Ma, Yongchang Peng, Duo Wang · Mar 27, 2026 · Citations: 0
Rubric RatingExpert Verification Automatic Metrics
To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains.
- Self-Preference Bias in Rubric-Based Evaluation of Large Language Models
José Pombal, Ricardo Rei, André F. T. Martins · Apr 8, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Llm As Judge
We present the first study of SPB in rubric-based evaluation, an increasingly popular benchmarking paradigm where judges issue binary verdicts on individual evaluation criteria, instead of assigning holistic scores or rankings.
- A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations
Andong Tan, Shuyu Dai, Jinglu Wang, Fengtao Zhou, Yan Lu · Mar 26, 2026 · Citations: 0
Expert Verification Human Eval
To address this gap, we introduce CPGBench, an automated framework benchmarking the clinical guideline detection and adherence capabilities of LLMs in multi-turn conversations.
- A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models
Maria Mahbub, Gregory M. Dams, Josh Arnold, Caitlin Rizy, Sudarshan Srinivasan · Apr 7, 2026 · Citations: 0
Expert Verification Automatic Metrics
Conventional evaluation methods rely heavily on annotation-intensive reference standards or incomplete structured data, limiting feasibility at population scale.
- Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models
Mikko Saukkoriipi, Nicole Hernandez, Jaakko Sahlsten, Kimmo Kaski, Otso Arponen · Mar 27, 2026 · Citations: 0
Expert Verification Automatic Metrics
Open-source large language models (LLMs) ranging from 4B to 70B parameters were benchmarked under fully offline conditions using 1,664 expert-annotated question-answer pairs derived from records of 183 patients.
- SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model
Guifeng Deng, Pan Wang, Jiquan Wang, Shuying Rao, Junyi Xie · Mar 22, 2026 · Citations: 0
Expert Verification Automatic Metrics
Expert evaluations further validated the quality of the model's reasoning, with mean scores exceeding 4.0/5.0 for factual accuracy, evidence comprehensiveness, and logical coherence.
- ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory
Zhuohan Ge, Haoyang Li, Yubo Wang, Nicole Hu, Chen Jason Zhang · Mar 27, 2026 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
To bridge this gap, we introduce ClinicalAgents, a novel multi-agent framework designed to simulate the cognitive workflow of expert clinicians.
- A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment
Sheng Liu, Long Chen, Zeyun Zhao, Qinglin Gou, Qingyue Wei · Mar 23, 2026 · Citations: 0
Expert Verification Automatic Metrics Multi Agent
We present Cerebra, an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.
- EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models
Shuang Zhou, Kai Yu, Zaifu Zhan, Huixue Zhou, Min Zeng · Mar 30, 2026 · Citations: 0
Expert Verification
In a clinician-AI collaboration setting, EpiScreen-assisted neurologists outperformed unaided experts by up to 10.9%.
- Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling
Qingyang Xu, Yaling Shen, Stephanie Fong, Zimu Wang, Yiwen Jiang · Apr 6, 2026 · Citations: 0
Red Team Simulation Env
The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions.
- OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs
Suraj Racha, Prashant Harish Joshi, Utkarsh Maurya, Nitin Yadav, Mridul Sharma · Mar 26, 2026 · Citations: 0
Rubric Rating Automatic Metrics
We highlight three primary challenges for LLMs in mental health - lack of high quality interpretable and knowledge grounded training data; training paradigms restricted to core capabilities, and evaluation of multi turn dialogue settings.
- DongYuan: An LLM-Based Framework for Integrative Chinese and Western Medicine Spleen-Stomach Disorders Diagnosis
Hua Li, Yingying Li, Xiaobin Feng, Xinyi Fu, Lifeng Dong · Mar 30, 2026 · Citations: 0
Pairwise Preference Web Browsing
While large language models (LLMs) offer new potential for medical applications, they face three major challenges in the context of integrative Chinese and Western medicine (ICWM): a lack of high-quality data, the absence of models capable…
- Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering
Elyas Irankhah, Samah Fodeh · Apr 8, 2026 · Citations: 0
Expert Verification Automatic Metrics
Third, results on the development set show that alignment accuracy is mainly limited by reasoning.
- Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy
Ruijie Yang, Yan Zhu, Peiyao Fu, Te Luo, Zhihua Wang · Apr 2, 2026 · Citations: 0
Expert Verification Automatic Metrics
Automatic speech recognition (ASR) is a critical interface for human-AI interaction in gastrointestinal endoscopy, yet its reliability in real-world clinical settings is limited by domain-specific terminology and complex acoustic…
- Learning Diagnostic Reasoning for Decision Support in Toxicology
Nico Oberländer, David Bani-Harouni, Tobias Zellner, Nassir Navab, Florian Eyer · Mar 31, 2026 · Citations: 0
Expert Verification Automatic Metrics
To address this, we present DeToxR (Decision-support for Toxicology with Reasoning), the first adaptation of Reinforcement Learning (RL) to emergency toxicology.
- Calibrated Confidence Expression for Radiology Report Generation
David Bani-Harouni, Chantal Pellegrini, Julian Lüers, Su Hwan Kim, Markus Baalmann · Mar 31, 2026 · Citations: 0
Expert Verification
In a clinical evaluation we show that ConRad's report level scores are well aligned with clinicians' judgment.
- Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
Zaifu Zhan, Mengyuan Cui, Rui Zhang · Mar 31, 2026 · Citations: 0
Critique Edit Automatic Metrics
Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by eliciting explicit intermediate reasoning; meanwhile,…
- TARo: Token-level Adaptive Routing for LLM Test-time Alignment
Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli · Mar 19, 2026 · Citations: 0
Pairwise Preference
Recent test-time alignment methods offer a lightweight alternative, but have been explored mainly for preference alignment rather than reasoning.
- CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram
Chang Liu, Changsheng Ma, Yongfeng Tao, Bin Hu, Minqiang Yang · Apr 8, 2026 · Citations: 0
Simulation Env Multi Agent
However, existing methods often rely on static cognitive profiles and omniscient single-agent simulation, failing to capture the dynamic, information-asymmetric nature of real therapy.
- Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization
Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mushfiqur Rahman, Niloy Kumar Mondal, Md. Mehedi Hasan Shawon · Mar 31, 2026 · Citations: 0
Human EvalAutomatic Metrics
Through controlled ablations on MIMIC-CXR benchmark, we show that selectively focusing on pathology-relevant visual patches rather than full images yields substantially better performance.
- Joint Optimization of Reasoning and Dual-Memory for Self-Learning Diagnostic Agent
Bingxuan Li, Simo Du, Yue Guo · Apr 8, 2026 · Citations: 0
Automatic Metrics Long Horizon
We propose SEA, a self-learning diagnostic agent with cognitively inspired dual-memory module.
- Agentic Automation of BT-RADS Scoring: End-to-End Multi-Agent System for Standardized Brain Tumor Follow-up Assessment
Mohamed Sobhi Jabal, Jikai Zhang, Dominic LaBella, Jessica L. Houk, Dylan Zhang · Mar 23, 2026 · Citations: 0
Automatic Metrics Multi Agent
This study evaluates an end-to-end multi-agent large language model (LLM) and convolutional neural network (CNN) system for automated BT-RADS classification.
- Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging
Mengxian Lyu, Cheng Peng, Ziyi Chen, Mengyuan Zhang, Jieting Li Lu · Apr 2, 2026 · Citations: 0
Expert Verification
Comprehensive evaluation across medical benchmarks and five clinical generation tasks (e.g., radiology and discharge summarization) shows that merged models can effectively mitigate catastrophic forgetting, preserve clinical domain…
- Brainstacks: Cross-Domain Cognitive Capabilities via Frozen MoE-LoRA Stacks for Continual LLM Learning
Mohammad R. Abu Ayyash · Apr 1, 2026 · Citations: 0
Expert Verification
We present Brainstacks, a modular architecture for continual multi-domain fine-tuning of large language models that packages domain expertise as frozen adapter stacks composing additively on a shared frozen base at inference.
- When Documents Disagree: Measuring Institutional Variation in Transplant Guidance with Retrieval-Augmented Language Models
Yubo Li, Ramayya Krishnan, Rema Padman · Mar 23, 2026 · Citations: 0
Pairwise Preference
Applied to 102 handbooks from 23 centers and 1,115 benchmark questions, the framework quantifies heterogeneity across four dimensions: question, topic, organ, and center.
- CrossTrace: A Cross-Domain Dataset of Grounded Scientific Reasoning Traces for Hypothesis Generation
Andrew Bouras, OMS-II Research Fellow · Mar 30, 2026 · Citations: 0
Llm As JudgeAutomatic Metrics
Fine-tuning Qwen2.5-7B-Instruct on CrossTrace via QLoRA yields substantial improvements over the untuned baseline: IAScore rises from 0.828 to 0.968 (GPT-4o judge) and from 0.716 to 0.888 (Claude Opus 4.5), structural compliance improves…
- HippoCamp: Benchmarking Contextual Agents on Personal Computers
Zhe Yang, Shulin Tian, Kairui Hu, Shuai Liu, Hoang-Nhat Nguyen · Apr 1, 2026 · Citations: 0
Automatic Metrics Tool Use
We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal file management.
- Efficient Failure Management for Multi-Agent Systems with Reasoning Trace Representation
Lingzhe Zhang, Tong Jia, Mingyu Wang, Weijie Hong, Chiming Duan · Mar 23, 2026 · Citations: 0
Automatic Metrics Multi Agent
Large Language Models (LLM)-based Multi-Agent Systems (MASs) have emerged as a new paradigm in software system design, increasingly demonstrating strong reasoning and collaboration capabilities.