Multi-Agent Specialist Reasoning with Two-Phase Verification for Calibrated Medical QA

Forum topic · 小凯 · 2026-03-27

Summary

A paper on arXiv (2603.24481) by John Ray Martinez proposes a multi-agent framework to improve confidence calibration in medical multiple-choice question answering. Miscalibrated confidence scores are a key obstacle to deploying AI in clinical settings, since overconfident models provide no useful signal for decision deferral. The framework combines four domain-specific specialist agents—respiratory, cardiology, neurology, and gastroenterology—each powered by Qwen2.5-7B-Instruct to generate independent diagnoses. These outputs are integrated through Two-Phase Verification and S-Score Weighted Fusion, aiming to improve both calibration and discrimination. The post was published on zhichai.net with a Chinese summary and the original English abstract, collected automatically on 2026-03-27.

Paper Overview

Field: NLP
Author: John Ray Martinez
Published: 2026-03-25
arXiv: 2603.24481

Abstract

Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful signal for deferral. The paper presents a multi-agent framework that combines domain-specific specialist agents with Two-Phase Verification and S-Score Weighted Fusion to improve both calibration and discrimination in medical multiple-choice question answering. Four specialist agents (respiratory, cardiology, neurology, gastroenterology) generate independent diagnoses using Qwen2.5-7B-Instruct.

Key Ideas

Problem: Overconfident models cannot support reliable decision deferral in clinical workflows.
Approach: A multi-agent system with four medical specialty agents producing independent diagnoses.
Verification: A Two-Phase Verification mechanism to check agent outputs.
Fusion: S-Score Weighted Fusion to combine specialist opinions, improving both calibration and discrimination.
Backbone model: Qwen2.5-7B-Instruct for each specialist agent.