@inproceedings{feng-etal-2026-trust,
    title = "Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search",
    author = "Feng, Tao  and
      Jiang, Xinke  and
      Hu, Xinyan  and
      Zhang, Yonggang  and
      Tao, Zhen  and
      Zhang, Wentao  and
      Liu, Boyang  and
      Jiang, Wenhao  and
      Wu, Chao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1276/",
    pages = "27664--27682",
    ISBN = "979-8-89176-390-6",
    abstract = "Agentic search augments large language models (LLMs) with external knowledge through reinforcement learning. However, existing approaches suffer from blind reliance on noisy retrieval and hallucination when both parametric and external knowledge fail{---}reflecting a lack of calibration regarding the model{'}s knowledge boundary. We propose Knowledge boundary Policy Optimization (KbPO), a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. KbPO introduces: (1) a semantic stability metric to delineate reliable parametric knowledge; (2) a four-quadrant taxonomy synthesising internal certainty with retrieval quality; and (3) a quadrant-based reward mechanism incentivising calibrated behaviour. We further adopt an iterative query evolution pipeline to construct boundary-probing training samples. Experiments on ten benchmarks demonstrate that KbPO outperforms strong baselines while exhibiting reduced hallucination rates."
}