@inproceedings{li-etal-2026-word,
    title = "From Word to World: Can Large Language Models be Implicit Text-based World Models?",
    author = "Li, Yixia  and
      Wang, Hongru  and
      Qiu, Jiahao  and
      Yin, Zhenfei  and
      Zhang, Dongdong  and
      Qian, Cheng  and
      Li, Zeping  and
      Ma, Xiaoteng  and
      Chen, Guanhua  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.366/",
    pages = "8084--8111",
    ISBN = "979-8-89176-390-6",
    abstract = "Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. World models promise to mitigate these limitations, but it remains unclear whether large language models can actually serve as reliable world models, and deliver concrete benefits to downstream agents. We investigate these questions in text-based environments, a controlled testbed that reframes language modeling as next-state prediction under interaction. We propose a three-level framework to evaluate LLM-based world models: (i) fidelity and consistency, (ii) scalability and robustness, and (iii) agent utility. Across five representative environments, we show that sufficiently trained world models capture coherent environment dynamics, scale predictably with data and model capacity, and unlock tangible agent improvements{---}for example, action verification boosts GPT-4o by 5.5{\%} on WebShop, and warm-started RL achieves a 15{\%} gain on SciWorld. Crucially, these benefits hinge on behavioral coverage and environment complexity, sharply characterizing when world modeling meaningfully advances agent learning."
}