DASH-Agent: Reliable Dissent Preservation for LLM Multi-Agent Reasoning

Large language model (LLM)-based multi-agent systems benefit from diverse reasoning and evidence, yet coordination can erase the information that makes this diversity useful. When several agents share an error, consensus may suppress a minority claim carrying the evidence needed to correct the final answer. Preserving every disagreement instead propagates noise and consumes verification budget. Rather than treating consensus or sparse communication as the endpoint, we study reliable dissent preservation: retaining evidence-bearing minority claims while filtering unsupported deviations under bounded communication. DASH-Agent implements this objective with a claim-level discovery–validation–verification pipeline. A task-conditioned interaction graph exposes candidate dissent, Dissent Signal Triage (DST) ranks candidates by novelty, task utility, reliability, and cost, and sparse local groups verify selected claims before synthesis. Extensive experiments across reasoning, multi-hop question answering, and tool-use benchmarks show improved task quality with lower communication, while claim-level analyses show stronger useful-dissent retention and incorrect-dissent suppression.