Ido Levy

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.