Multi-Agent LLM Systems

LLM: Large Language Model

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85 papers in the last four weeks, up 107% on the four weeks before. 0.8% of all new papers.

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MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis
Date pendingYuqi Zhan, Xinyue Wu, Tianyu Lin +6Multi-Agent LLM Systems Medical Report Generation
Hangzhou Dianzi University · Zhejiang University · Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents’ Health and Diseases
Emergent Risks in Generative Multi-Agent Systems
Date pendingYue Huang, Yu Jiang, Wenjie Wang +12Multi-Agent LLM Systems Multi-Agent Systems
University of Notre Dame · LMU Munich · University of Washington +7
Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
Date pendingRakibul Hasan Rajib, Mengxin Zheng, Qian LouMulti-Agent LLM Systems Multi-Agent System Optimization
University of Central Florida
NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis
Date pendingWuche Liu, Yiran Qiao, Linlin Hou +4Multi-Agent LLM Systems AI for Science
Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA · Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA · Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
Date pendingNiloy Kumar Mondal, Md Rizwan ParvezMulti-Agent LLM Systems Large Language Model-Guided Optimization
Bangladesh University of Engineering and Technology · Qatar Computing Research Institute, HBKU, Doha, Qatar.

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MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

Yuqi Zhan, Xinyue Wu, Tianyu Lin, Yutong Bao+5

Hangzhou Dianzi University · Zhejiang University · Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents’ Health and Diseases

Multi-Agent LLM Systems Medical Report Generation Healthcare Medical Diagnosis Multi-Agent Debate Clinical Reasoning+1

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Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions. Large language models have advanced medical text understanding and generation. Yet their clinical use remains limited by weak evidence grounding, opaque reasoning, and inconsistent links among differential diagnosis, final diagnosis, diagnostic basis, and treatment planning. We introduce MedCollab, a multi-agent framework for full-cycle clinical diagnosis and report generation. MedCollab coordinates specialist and examination agents according to patient records. It structures agent deliberation with an Issue-Based Information System (IBIS) protocol, so that each diagnostic position is supported by patient-specific evidence and medical knowledge. It also builds Hierarchical Disease Relation Chains (HDRC) to connect accepted hypotheses through progression, complication, and comorbidity relations. During multi-round deliberation, a verifier-guided consensus module evaluates evidence support, medical plausibility, and logical conflicts. It then adjusts agent contributions and filters unsupported reasoning. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms leading LLMs and medical multi-agent baselines in diagnostic accuracy, evidence consistency, and clinical reasoning quality. These results indicate that structured and auditable collaboration can produce more faithful and clinically coherent diagnostic reports.

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Related topics

Latest papers 665

MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

Emergent Risks in Generative Multi-Agent Systems

Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems

NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis

QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language