工具使用智能體判定結果無用後，仍很少停止查詢
Judged Useless, Queried Anyway: Tool-Using Agents Rarely Turn Their Own Evidence Judgments into Stopping Decisions
研究發現，受測七個智能體有 97-100% 的情況把失效來源的結果判為無用，但大多數很少據此停止查詢。只有在實驗框架強制加入整合步驟、要求智能體連續五次判定結果無用後作答時，停止行為才會跟隨證據；這提高了所有模型在失效來源下的成功率，而且預算加倍時停止點仍固定。以 300 條全新問題進行的預先註冊重複實驗，亦確認判斷與停止行為的分離及該規則的效果。
Published on Oct 5
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Abstract
An agent whose tool keeps returning nothing useful should stop relying on it. In a retrieval environment with controlled source failures, we separate how agents judge results from what they do. We compare stopping at the same step after longer and shorter runs of results the agent judged useless; this contrast is zero for clock- or deadline-driven stopping. Where we record their judgments, the seven agents we test call a failing source's results useless 97-100% of the time, yet most of them rarely stop on that judgment. Prompt cues change when they stop but not what they stop on. Permission to answer from memory and a reasoning mode can bring early stops regardless of evidence, a stated budget moves the 7-8B models' stops to the deadline, and a stopping rule or call cost in the prompt is followed at most partly. Stopping follows the evidence only when the harness enforces an integration step that makes the agent answer after five consecutive results it judged useless. This step raises failing-source success for every model, keeps the stopping point fixed when the budget doubles, and needs no extra judgment call when the agent states its judgments. A pre-registered replication on 300 fresh questions confirms the dissociation and the rule's effect.
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來源：HuggingFace Daily Papers（社區熱門論文） · huggingface.co