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ANTMAN：面向大规模信息空间多智能体导航的自适应需求追踪框架
AI 导读
ANTMAN 是一个以动态未解信息需求为协调单元的自适应多智能体框架，通过可修订的 Need Graph 追踪未解需求、已积累证据与搜索进度，并据此控制 worker 选择、路由和任务级恢复。在多文档问答、长上下文扩展和结构化导航实验中，可搜索上下文扩大 16 倍时，ANTMAN 的活跃协调量仅增加 1.23 倍，而基于静态分区的基线超过 15 倍，同时保持较强答案质量。
正文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.33326 [cs.AI] |
| (or arXiv:2609.33326v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.33326 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jerry Wang [view email]
[v1]
Sun, 27 Sep 2026 07:55:15 UTC (412 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org