ANTMAN：大规模信息空间中多智能体导航的自适应需求追踪
ANTMAN: Adaptive Need Tracking for Multi-Agent Navigation in Large Information Spaces
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中文总结 AI 辅助
针对大规模信息空间中多智能体导航的协调开销问题，提出基于需求图的自适应框架ANTMAN，在上下文扩大16倍时仅增加1.23倍协调，优于分区基线15倍以上。
中文摘要 AI 辅助
信息寻求智能体越来越多地运行在因规模过大而无法穷尽处理的信息空间中。然而，许多多智能体系统将计算组织在可用空间的静态分区之上，导致协调开销随信息的分割方式增长，而非随查询仍需要的部分增长。我们提出ANTMAN，一种自适应协调框架，将不断演化的未解决信息需求视为运行时协调的基本单位。ANTMAN维护一个可修订的需求图，追踪未解决的需求、累积的证据、先前的尝试和搜索进度，并利用该状态来控制工人选择、路由以及在新证据被发现时的任务局部恢复。通过将协调策略与特定于底层的搜索接口分离，同一需求条件机制可以跨不同信息空间运行。跨多文档问答、受控长上下文扩展和现实结构化导航的实验表明，ANTMAN在各种设置下均保持有效性，包括当执行被委托给规模显著较小的工人模型时。在可搜索上下文增加16倍的情况下，ANTMAN仅将主动协调增加1.23倍，而分区驱动的基线则增加超过15倍，同时保持强大的答案质量。
英文摘要
Information-seeking agents increasingly operate over information spaces that are too large to process exhaustively. Yet many multi-agent systems organize computation around static partitions of the available space, causing coordination to grow with how information is segmented rather than with what the query still requires. We introduce ANTMAN, an adaptive coordination framework that treats evolving unresolved information needs as the unit of runtime coordination. ANTMAN maintains a revisable Need Graph that tracks unresolved requirements, accumulated evidence, prior attempts, and search progress, and uses this state to control worker selection, routing, and task-local recovery as new evidence is discovered. By separating the coordination policy from substrate-specific search interfaces, the same need-conditioned mechanism can operate across different information spaces. Experiments across multi-document question answering, controlled long-context scaling, and realistic structured navigation show that ANTMAN remains effective across settings, including when execution is delegated to substantially smaller worker models. Under a 16x increase in searchable context, ANTMAN increases active coordination by only 1.23x, compared with more than 15x for partition-driven baselines, while preserving strong answer quality.
