MedDAC: Learning Directional Communication via Correction and Harm Modeling for Medical Multi-Agent Reasoning

Multi-agent reasoning relies on communication among agents, yet peer messages can either correct an erroneous receiver or mislead an initially correct one. Moreover, agents may revise their answers through self-reflection alone, making it difficult to distinguish peer influence from ordinary second-pass reasoning. We present MedDAC, a framework for learning directional agent communication policies by modeling both corrective and harmful peer effects. MedDAC evaluates receiver-specific sender-subset interventions against matched self-review to estimate the incremental local and system-level effects of communication actions. MedDAC-Static aggregates these effects to learn a fixed sparse communication graph under a message budget, while MedDAC-Dynamic predicts question-conditioned communication actions from the current multi-agent state. Across five medical QA benchmarks, the complete MedDAC-Static pipeline achieves 74.57% macro-average accuracy, compared with 69.77% for single-agent inference. Under the same Majority Vote aggregation, its six-message policy achieves 73.13%, comparable to the 72.99% obtained with 12-message Full Communication. These results show that modeling both corrective and harmful peer effects enables selective communication while maintaining competitive reasoning accuracy with substantially fewer directed messages.