@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{\"u}r, 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://aclanthology.org/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."
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%0 Conference Proceedings
%T Current Agents Fail to Leverage World Model as Tool for Foresight
%A Qian, Cheng
%A Acikgoz, Emre Can
%A Li, Bingxuan
%A Chen, Xiusi
%A Zhang, Yuji
%A He, Bingxiang
%A Luo, Qinyu
%A Tur, Gokhan
%A Hakkani-Tür, Dilek
%A Li, Yunzhu
%A Ji, Heng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F qian-etal-2026-current
%X 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.
%U https://aclanthology.org/2026.acl-long.623/
%P 13686-13723
Markdown (Informal)
[Current Agents Fail to Leverage World Model as Tool for Foresight](https://aclanthology.org/2026.acl-long.623/) (Qian et al., ACL 2026)
ACL
- Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, and Heng Ji. 2026. Current Agents Fail to Leverage World Model as Tool for Foresight. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13686–13723, San Diego, California, United States. Association for Computational Linguistics.