One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction

ArXi:2604.00085v1 Announce Type: new Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement.