When Consensus Masks Risk: Energy-Guided Control of Diagnostic Discussion

Multi-agent deliberation can improve high-stakes classification, yet consensus alone does not establish safety. In diagnostic-style decisions, agents with asymmetric expertise may reach agreement while high-cost alternatives remain unresolved, a failure mode we call premature diagnostic closure. We cast this process as a protocol-control problem without online ground truth: using agents' reports, optional justifications, calibration confusion matrices, and an asymmetric loss, a mediator must decide whether to continue, challenge, certify, or escalate. We propose Diagnostic Consensus Energy Minimization (D-CEM), a loss-aware controller that interprets each agent's confusion matrix as a map of plausible differential diagnoses. Given current reports, D-CEM forms confusion-induced posteriors, identifies the most dangerous plausible miss under the loss, and computes a diagnostic consensus energy combining posterior disagreement, expected harm, and loss-aware margin. The policy continues while risk decreases, issues targeted differential challenges, certifies only low-energy large-margin decisions, and escalates when high-risk deliberation stagnates. We derive loss-sensitive risk bounds, calibrated decision guarantees, and cost-aware stopping bounds. Experiments on synthetic and clinical tasks show that D-CEM reduces high-risk misses and harmful consensus while improving safety–cost trade-offs over aggregation baselines.