Paper Detail
Yixia Li, Hongru Wang, Peng Lai, Zhiwen Ruan, He Zhu, Youxin Zhu, Ganlong Zhao, Minda Hu, Yun Chen, Sibei Yang, Peng Li, Jeff Z. Pan, Jia Pan, Guanhua Chen, Yang Liu, Guanbin Li
Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve. This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply, thereby supporting planning, efficient learning, and principled evaluation. We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle: (1) Foundations, defining text world models and characterizing them by state representation and grounding domain; (2) Construction, taxonomizing LLM-as-WM and code-as-WM paradigms and reviewing methods for building them; (3) Application, examining how world models support agents at training time through experience synthesis and at inference time through planning, verification, and adaptation; and (4) Evaluation, covering both evaluation of the world model itself and its use as an evaluation environment for agents. We aim to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.
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@article{li2026bridging,
title = {Bridging the Agent-World Gap: Text World Models for LLM-based Agents},
author = {Yixia Li and Hongru Wang and Peng Lai and Zhiwen Ruan and He Zhu and Youxin Zhu and Ganlong Zhao and Minda Hu and Yun Chen and Sibei Yang and Peng Li and Jeff Z. Pan and Jia Pan and Guanhua Chen and Yang Liu and Guanbin Li},
year = {2026},
abstract = {Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve. This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API respo},
url = {https://arxiv.org/abs/2606.09032},
keywords = {cs.CL},
eprint = {2606.09032},
archiveprefix = {arXiv},
}
{}