# Need Graph

The work adds a content axis to agent proactivity—horizontal proactivity pursues unstated information the current context already identifies, vertical proactivity pursues needs only earlier evidence reveals—scores runs against a need graph recovered mechanically from multi-hop benchmarks' own decompositions with no model judge, and proposes Q&D (questioner and drafter), which forks a recorded run at one step, samples eight candidate questions, continues each to the end, and prefers the question whose continuation retrieves more required evidence, so no reward model or judge is needed; on held-out splits of MuSiQue, StrategyQA and 2WikiMultiHopQA, at equal retrieval spend the trained 8B questioner recovers more required evidence than the same model prompted (89.5% versus 78.

The work adds a content axis to agent proactivity—horizontal proactivity pursues unstated information the current context already identifies, vertical proactivity pursues needs only earlier evidence reveals—scores runs against a need graph recovered mechanically from multi-hop benchmarks' own decompositions with no model judge, and proposes Q&D (questioner and drafter), which forks a recorded run at one step, samples eight candidate questions, continues each to the end, and prefers the question whose continuation retrieves more required evidence, so no reward model or judge is needed; on held-out splits of MuSiQue, StrategyQA and 2WikiMultiHopQA, at equal retrieval spend the trained 8B questioner recovers more required evidence than the same model prompted (89.5% versus 78.

The work introduces ANTMAN, a multi-agent information-seeking framework that treats evolving unresolved information needs, rather than input partitions, as the unit of runtime coordination, maintaining a revisable Need Graph that tracks unresolved requirements, accumulated evidence, prior attempts, and search progress to control worker selection, routing, and task-local recovery; across multi-document QA, controlled long-context scaling, and structured navigation, ANTMAN increases active coordination by only 1.23x under a 16x increase in searchable context, compared with more than 15x for partition-driven baselines, while preserving strong answer quality and retaining most of its performance when execution is delegated to substantially smaller 8B worker models.

The work introduces ANTMAN, a multi-agent information-seeking framework that treats evolving unresolved information needs, rather than input partitions, as the unit of runtime coordination, maintaining a revisable Need Graph that tracks unresolved requirements, accumulated evidence, prior attempts, and search progress to control worker selection, routing, and task-local recovery; across multi-document QA, controlled long-context scaling, and structured navigation, ANTMAN increases active coordination by only 1.23x under a 16x increase in searchable context, compared with more than 15x for partition-driven baselines, while preserving strong answer quality and retaining most of its performance when execution is delegated to substantially smaller 8B worker models.