Who Else Should See It? Group-Aware Evidence Routing for Multi-Agent LLMs

In multi-agent search systems, deciding whether evidence is useful to one agent does not determine how widely it should be shared. We present MERGE (Multi-Agent Evidence Routing with Group Effects), a router that learns how the benefit of sharing evidence with an additional agent depends on how many agents already hold it. Using paired interventions that differ by one additional agent, MERGE combines this learned signal with agent-specific usefulness and communication cost, then selects routing decisions jointly. Across nine QA dataset–backbone settings, MERGE achieves the highest mean F1 and EM scores. On the primary HotpotQA/Qwen2.5-14B setting, it reaches 0.716 F1, improving by 2.4 points over the strongest controlled baseline while using 11.8% fewer tokens. Analysis reveals early diminishing returns from redundant sharing and a late-stage effect tied to the fixed voting rule. Together, these results show that routing quality depends on both how information is distributed across the team and how the team combines its final decisions. Code is available at https://anonymous.4open.science/r/merge-routing-053E/.