@inproceedings{qian-etal-2026-current,
    title = "Current Agents Fail to Leverage World Model as Tool for Foresight",
    author = "Qian, Cheng  and
      Acikgoz, Emre Can  and
      Li, Bingxuan  and
      Chen, Xiusi  and
      Zhang, Yuji  and
      He, Bingxiang  and
      Luo, Qinyu  and
      Tur, Gokhan  and
      Hakkani-T{\textbackslash}{''}ur, Dilek  and
      Li, Yunzhu  and
      Ji, Heng",
    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.623/",
    pages = "13686--13723",
    ISBN = "979-8-89176-390-6",
    abstract = "Agents built on vision-language models increasingly face tasks that demand anticipating future states rather than relying on short-horizon reasoning. Generative world models offer a promising remedy: agents could use them as external simulators to foresee outcomes before acting. This paper empirically examines whether current agents can leverage such world models as tools to enhance their cognition. Across diverse agentic and visual question answering tasks, we observe that some agents rarely invoke simulation (fewer than 1{\%}), frequently misuse predicted rollouts (approximately 15{\%}), and often exhibit inconsistent or even degraded performance (up to 5{\%}) when simulation is available or enforced. Attribution analysis further indicates that the primary bottleneck lies in the agents' capacity to decide when to simulate, how to interpret predicted outcomes, and how to integrate foresight into downstream reasoning. These findings underscore the need for mechanisms that foster calibrated, strategic interaction with world models, paving the way toward more reliable anticipatory cognition in future agent systems."
}