多智能体协作结构不用手动调了，MANTA 在推理时自动改拓扑，五个基准平均分 74.0，超基线 5.8 个点。
MANTA 是一个多智能体网络拓扑自适应框架，让通信结构在推理时自我进化。MANTA 在执行前初始化任务条件拓扑，运行中监测协作轨迹并应用有界结构更新。在信息检索、工具使用、规划、工作流执行和数学推理五个基准上，MANTA 平均分达 74.0，超过最强基线 5.8 个百分点。它在 PlanCraft 任务上也取得最优成绩。
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
Large language model-based multi-agent systems improve complex problem solving through task decomposition, agent specialization, information exchange, and intermediate validation. However, existing systems typically treat communication topology as a fixed design choice or an offline optimization target. We introduce MANTA, a framework for Multi-Agent Network Topology Adaptation that enables communication structures to self-evolve at inference time. Before execution, MANTA initializes a task-conditioned topology from prior structural experience. During deployment, it monitors collaboration traces and applies bounded structural updates when the current organization becomes insufficient. These updates can modify agent roles, communication links, execution order, information visibility, and validation pathways while preserving the task interface and agent budget. We evaluate MANTA against representative single-agent and multi-agent baselines on five benchmarks spanning information seeking, tool use, planning, workflow execution, and mathematical reasoning. MANTA achieves the highest average score of 74.0, outperforming the strongest baseline by 5.8 percentage points and obtaining the best result on PlanCraft. These results show that inference-time self-improvement can extend to the architecture of collaboration itself.