mlsanityData to 14 Oct 2026

A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support

arXiv 2609.05069 ↗First posted 4 Sep 2026cs.CL cs.AIcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

Large language models (LLMs) show potential for medical tasks, but their single-turn question-answer format does not reflect how clinical diagnosis is performed in practice. As a result, they remain limited in complex diagnostic settings. We developed Debate-Mixture-of-Agents (DMoA), a novel multi-agent framework that structures role-based interaction to support iterative diagnostic reasoning. Base models and DMoA were evaluated on 297 rare disease cases and 1,719 challenging cases. Across both datasets, DMoA improved most likely diagnosis accuracy by 10.21 percentage points and safety rate by 11.36 percentage points over GPT-4o baseline. Ablation experiments showed that the gains were not simply due to the use of more models or longer outputs, but also reflected the contribution of the structured workflow. Further analyses examined how framework design, base model choice, and token budget affected performance. DMoA performed better with a 4*2 structure, stronger base models, and a larger token budget. These findings demonstrate the potential of DMoA for clinical tasks and suggest further investigation of multi-agent frameworks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 21 of its 47 references that are in the map. First posted on arXiv 4 Sep 2026.

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21 of 47 references.

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Same direction, newest first

  1. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments13 May

Other references · 20

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Let's Verify Step by Stepcited by 3,789
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  4. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  5. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  6. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 438
  7. 2018AI safety via debatecited by 400
  8. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughtcited by 337
  9. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 316
  10. 2024RLHF Workflow: From Reward Modeling to Online RLHFcited by 267
  11. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  12. 2023Towards Accurate Differential Diagnosis with Large Language Modelscited by 116
8 more
  1. 2025Multi-Agent Design: Optimizing Agents with Better Prompts and Topologiescited by 113
  2. 2025MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphscited by 110
  3. 2023Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicinecited by 83
  4. 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Surveycited by 83
  5. 2024Large Language Models for Disease Diagnosis: A Scoping Reviewcited by 51
  6. 2026Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversitycited by 29
  7. 2025Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Modelscited by 2
  8. 2026Guided Collaboration in Heterogeneous LLM-Based Multi-Agent Systems via Entropy-Based Understanding Assessment and Experience Retrievalcited by 2

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