@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://aclanthology.org/2025.acl-long.39/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents
%A Qian, Cheng
%A Han, Peixuan
%A Luo, Qinyu
%A He, Bingxiang
%A Chen, Xiusi
%A Zhang, Yuji
%A Du, Hongyi
%A Yao, Jiarui
%A Yang, Xiaocheng
%A Zhang, Denghui
%A Li, Yunzhu
%A Ji, Heng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F qian-etal-2025-escapebench
%X 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.
%R 10.18653/v1/2025.acl-long.39
%U https://aclanthology.org/2025.acl-long.39/
%U https://doi.org/10.18653/v1/2025.acl-long.39
%P 798-820
Markdown (Informal)
[EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents](https://aclanthology.org/2025.acl-long.39/) (Qian et al., ACL 2025)
ACL
- Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, and Heng Ji. 2025. EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 798–820, Vienna, Austria. Association for Computational Linguistics.