SearchJev: A Calibrated System-1 Model for Search Agents
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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SearchHorizonBench is a new benchmark that evaluates how AI agents decide whether the cost of an additional search is justified by its expected benefit. It provides 45,632 executions on 3,168 questions, using paired continuations and repeated runs to separate true search gain from generation noise. The dataset shows that historical calibration can reduce Brier error by 16.2% and that free‑selection observations offer a 2.49‑point utility advantage over a constant action, highlighting the importance of accurate search‑value estimation.
AdaSearch introduces a two‑stage reinforcement learning framework that separates problem solving from the decision to search in large language models. By using an F1‑based decision metric, it explicitly evaluates when external search is needed, reducing unnecessary search calls while maintaining high question‑answering performance. Experiments show that AdaSearch improves search‑decision quality with only a minor impact on accuracy compared to always‑search strategies.
The paper introduces ZEST-Search, a zero-label framework that trains search agents by co-evolving task generation and solver learning. It constructs validated search tasks online from unlabeled questions, using a heterogeneous proposer to adapt task difficulty to the solver’s evolving capabilities. The approach rewards the proposer for intermediate difficulty and the solver for tool-aware performance, leading to improved search efficiency and accuracy across multiple question‑answering benchmarks.
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