Communication as Evidence Routing: Adaptive Relational Graphs via Optimal Transport for Multi-Agent LLM Reasoning

Multi-agent large language model (LLM) systems offer a promising paradigm for complex reasoning by integrating complementary expertise across multiple models. However, existing agent communication strategies rely on either free-form interaction or answer-level aggregation, making reasoning susceptible to dominant-answer collapse, where prevailing answers prematurely suppress valuable dissenting evidence. To address this, we reinterpret multi-agent communication as evidence routing and propose Adaptive Relational Graphs via Optimal Transport (ARGOT), a relational graph framework for multi-agent LLM reasoning. ARGOT represents agents’ initial responses as evidence distributions and uses optimal transport to construct a task-adaptive graph comprising support and challenge relations. These typed relations enable conflict-aware message passing, allowing consistent and conflicting evidence to be propagated and integrated differently during reasoning. A trajectory-level verifier then selects the final answer by evaluating routed evidence and reasoning trajectories, rather than relying solely on answer frequency. Across six reasoning benchmarks, ARGOT achieves the highest average performance and ranks first on four tasks. Further analyses show it preserves informative disagreement, improves evidence aggregation, and mitigates premature consensus. These results establish structured evidence routing as an effective, principled approach to more reliable multi-agent LLM reasoning.