@inproceedings{qian-etal-2025-escapebench,
    title = "{E}scape{B}ench: Towards Advancing Creative Intelligence of Language Model Agents",
    author = "Qian, Cheng  and
      Han, Peixuan  and
      Luo, Qinyu  and
      He, Bingxiang  and
      Chen, Xiusi  and
      Zhang, Yuji  and
      Du, Hongyi  and
      Yao, Jiarui  and
      Yang, Xiaocheng  and
      Zhang, Denghui  and
      Li, Yunzhu  and
      Ji, Heng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.39/",
    pages = "798--820",
    ISBN = "979-8-89176-251-0",
    abstract = "Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative adaptation in unfamiliar environments. To address this, we introduce EscapeBench{---}a benchmark suite of room escape game environments designed to challenge agents with creative reasoning, unconventional tool use, and iterative problem-solving to uncover implicit goals. Our results show that current LM models, despite employing working memory and Chain-of-Thought reasoning, achieve only 15{\%} average progress without hints, highlighting their limitations in creativity. To bridge this gap, we propose EscapeAgent, a framework designed to enhance creative reasoning through Foresight (innovative tool use) and Reflection (identifying unsolved tasks). Experiments show that EscapeAgent can execute action chains over 1,000 steps while maintaining logical coherence. It navigates and completes games with up to 40{\%} fewer steps and hints, performs robustly across difficulty levels, and achieves higher action success rates with more efficient and innovative puzzle-solving strategies."
}