让多个AI助手自动优化协作方式和自身能力，提升问答准确率。
EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering
- 通过闭环迭代优化路由与提示，让助手和路由系统互相提升。
- 动态调整参与问答的助手数量，根据问题复杂度决定协作规模。
- 在5个数据集上超越现有最佳方法，适合复杂多步骤问答场景。
大型语言模型代理常具备互补优势，使路由成为多代理问答的有前景方案。然而，现有路由方法存在两大局限：通常在固定代理池上优化，未能提升代理本身能力；且依赖僵化的协作模式，无法根据查询动态调整参与代理数量。本文提出EvolveRouter，一种可训练框架，通过联合优化代理质量与协作结构来解决上述问题。首先，将基于图的查询路由与定向指令优化结合，在闭环协同演化过程中，路由诊断指导代理改进，而优化后的代理又为路由提供更清晰的监督信号。其次，引入自适应推理策略，通过路由器加权的答案一致性动态确定每个查询的有效协作规模。该设计显著提升了多代理推理的能力与效率。在五个问答基准上的实验表明，EvolveRouter在F1和精确匹配指标上持续优于当前最优路由基线，进一步分析证实了闭环优化与自适应协作的收益。
原文摘要 · Abstract (English)
Large language model agents often exhibit complementary strengths, making routing a promising approach for multi-agent question answering. However, existing routing methods remain limited in two important ways: they typically optimize over a fixed pool of agents without improving the agents themselves, and they often rely on rigid collaboration schemes that cannot adapt the number of participating agents to the query. We propose EvolveRouter, a trainable framework that addresses both limitations by jointly improving agent quality and collaboration structure. First, EvolveRouter couples graph-based query routing with targeted instruction refinement in a closed-loop co-evolution process, allowing router diagnostics to guide agent improvement while refined agents provide cleaner supervision for routing. Second, it introduces an adaptive inference strategy that dynamically determines the effective collaboration size for each query through router-weighted answer agreement. Together, these designs enable more capable and more efficient multi-agent reasoning. Experiments on five question answering benchmarks show that EvolveRouter consistently outperforms SOTA routing baselines in both F1 and exact match, while further analysis confirms the benefits of closed-loop refinement and adaptive collaboration.
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