Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology

• URL: http://arxiv.org/abs/2608.11420v1
• Date: Tue, 11 Aug 2026 20:38:03 GMT
• Title: Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology
• Abstract summary: Medical diagnostic reasoning is a high-impact use case for LLMs.<n>OpenAI reports that more than 5% of ChatGPT messages globally are healthcare-related.<n>We introduce Social Chain of Thought (SCoT),a multi-round pipeline for medical differential diagnosis.
• Score: 21.116270925609477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users. When OpenAI (2026) reports that more than 5% of ChatGPT messages globally are healthcare-related, the transparency of these systems becomes a serious design concern. This is especially true for complex cases, where differential diagnosis often requires integrating multiple forms of specialist reasoning. Existing work has proposed multi-agent approaches to medical diagnosis, but it remains unclear when such systems are needed, why they help, and where they outperform monolithic inference. We introduce Social Chain of Thought (SCoT),a multi-round pipeline for medical differential diagnosis that structures multi-agent interaction as a deliberative framework for collabora. tive LLM reasoning. Evaluating SCoT against single-agent baselines, one-agent pipeline ablations, and best-of-n scaling, we show that its recall advantage is not reproduced by monolithic inference alone. SCoT is most successful in the hardest diagnostic cases, where multiple rounds of specialist conversation help recover ground-truth diagnoses and converge on a higher-recall differential.

Related papers

• MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis [61.266743965495884]
  We introduce MedReaMM, a benchmark designed to evaluate models' ability to synthesize heterogeneous clinical evidence.<n>MedReaMM comprises 625 expert-validated cases with an average of 2.79 medical images per case and a total of 1,042 standardized diagnoses annotated with ICD-11 codes.<n>We evaluate 23 Large Multimodal Models (LMMs) and find that most achieve diagnostic accuracy scores below 50%, underscoring a substantial gap in multimodal diagnostic synthesis capability.
  arXiv  Detail & Related papers  (2026-08-23T09:37:53Z)
• MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems [38.36687601516826]
  Multi-Agent Systems (MAS) show potential for complex clinical decision support.<n>Current MAS medical research suffers from non-uniform data ingestion and inconsistent visual-reasoning evaluation.<n>We present MedMASLab, a unified framework and benchmarking platform for seamless multi-agent systems.
  arXiv  Detail & Related papers  (2026-03-10T17:03:11Z)
• MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus [24.19892707167392]
  Existing AI approaches for clinical diagnosis often lack transparency, structured reasoning, and deployability.<n>We propose MedCoRAG, an end-to-end framework that generates diagnostic hypotheses from standardized abnormal findings.<n>It then constructs a patient-specific evidence package by jointly retrieving and pruning UMLS knowledge graph paths and clinical guidelines.
  arXiv  Detail & Related papers  (2026-03-05T12:58:45Z)
• MedRoute: RL-Based Dynamic Specialist Routing in Multi-Agent Medical Diagnosis [49.78267008828593]
  In clinical practice, diagnosis is performed by multiple specialists, each contributing domain-specific expertise.<n>Current approaches in this emerging area, typically relying on static or predefined selection of various specialists, cannot be adapted to the changing practical scenario.<n>We propose MedRoute, a flexible and dynamic multi-agent framework that comprises of a collaborative system of specialist LMM agents.
  arXiv  Detail & Related papers  (2026-02-05T19:50:04Z)
• MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement [63.82954136824963]
  Medical Vision-Language Models excel at perception tasks with complex clinical reasoning required in real-world scenarios.<n>We propose a novel reasoning MedVLM that addresses these challenges through domain-specific adaptation and guideline reinforcement.
  arXiv  Detail & Related papers  (2026-01-16T02:32:07Z)
• MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models [26.152027922514957]
  textscMedLA is a logic-driven multi-agent framework built on large language models.<n>Agents engage in a graph-guided discussion to compare and iteratively refine their logic trees.<n>We demonstrate that textscMedLA consistently outperforms both static role-based systems and single-agent baselines.
  arXiv  Detail & Related papers  (2025-09-28T08:06:39Z)
• End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning [52.12425911708585]
  Deep-DxSearch is an agentic RAG system trained end-to-end with reinforcement learning (RL)<n>In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources.<n> Experiments demonstrate that our end-to-end RL training framework consistently outperforms prompt-engineering and training-free RAG approaches.
  arXiv  Detail & Related papers  (2025-08-21T17:42:47Z)
• Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree [14.013981070330153]
  We propose Tree-of-Reasoning (ToR), a novel multi-agent framework designed to handle complex scenarios.<n>Specifically, ToR introduces a tree structure that can clearly record the reasoning path of large language models (LLMs) and the corresponding clinical evidence.<n>At the same time, we propose a cross-validation mechanism to ensure the consistency of multi-agent decision-making.
  arXiv  Detail & Related papers  (2025-08-05T03:31:28Z)
• An Agentic System for Rare Disease Diagnosis with Traceable Reasoning [69.46279475491164]
  We introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM)<n>DeepRare generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning.<n>The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases.
  arXiv  Detail & Related papers  (2025-06-25T13:42:26Z)
• Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making [80.94208848596215]
  We present a new concept called Catfish Agent, a role-specialized LLM designed to inject structured dissent and counter silent agreement.<n>Inspired by the catfish effect'' in organizational psychology, the Catfish Agent is designed to challenge emerging consensus to stimulate deeper reasoning.
  arXiv  Detail & Related papers  (2025-05-27T17:59:50Z)
• MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow [14.478357882578234]
  In modern medicine, clinical diagnosis relies on the comprehensive analysis of primarily textual and visual data.<n>Recent advances in large Vision-Language Models (VLMs) and agent-based methods hold great potential for medical diagnosis.<n>We propose MedAgent-Pro, a new agentic reasoning paradigm that follows the diagnosis principle in modern medicine.
  arXiv  Detail & Related papers  (2025-03-21T14:04:18Z)
• Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis [30.943705201552643]
  We propose a framework to model the diagnosis process in the real world by adaptively fusing probability distributions of agents over potential diseases. Our approach requires significantly less parameter updating and training time, enhancing efficiency and practical utility.
  arXiv  Detail & Related papers  (2024-01-29T12:25:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.