@inproceedings{wang-etal-2026-memwm,
title = "{M}em{WM}: Memory-Augmented Text-Based World Model",
author = "Wang, Yujun and
Zhang, Tao and
Bi, Jinhe and
Aniri and
Ye, Wenxuan and
Liu, Boliang and
Yan, Sikuan and
Wang, Shuning and
Zhou, Xuebing and
Pirk, Soren and
Schuetze, Hinrich and
Ma, Yunpu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2221/",
pages = "50542--50564",
ISBN = "979-8-89176-459-0",
abstract = "World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can still omit task-critical facts, corrupt product attributes, or apply incorrect transition rules. To address such systematic prediction errors, we introduce MemWM, a memory-augmented text-based world model. MemWM uses world memory, a curated memory bank of transition rules, state caches, and hard-to-predict facts, to condition next-state imagination. We evaluate factual state preservation with Structured State Fidelity (SSF), which scores predicted states through benchmark-specific facts and fields. Compared with SFT, memory-augmented training improves SSF by up to 206.3{\%}. In the full planning setting, we keep the policy model frozen and provide policy-side world skill: retrieved task-level skills and step-wise corrective guidance for action selection. Across ALFWorld, WebShop, and ScienceWorld, memory-augmented agents improve downstream success over an SFT-trained world-model agent, with up to a 65.4{\%} relative gain. Sensitivity analyses further show that retrieved memory improves task success and efficiency under different memory and action-budget settings."
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<abstract>World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can still omit task-critical facts, corrupt product attributes, or apply incorrect transition rules. To address such systematic prediction errors, we introduce MemWM, a memory-augmented text-based world model. MemWM uses world memory, a curated memory bank of transition rules, state caches, and hard-to-predict facts, to condition next-state imagination. We evaluate factual state preservation with Structured State Fidelity (SSF), which scores predicted states through benchmark-specific facts and fields. Compared with SFT, memory-augmented training improves SSF by up to 206.3%. In the full planning setting, we keep the policy model frozen and provide policy-side world skill: retrieved task-level skills and step-wise corrective guidance for action selection. Across ALFWorld, WebShop, and ScienceWorld, memory-augmented agents improve downstream success over an SFT-trained world-model agent, with up to a 65.4% relative gain. Sensitivity analyses further show that retrieved memory improves task success and efficiency under different memory and action-budget settings.</abstract>
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%0 Conference Proceedings
%T MemWM: Memory-Augmented Text-Based World Model
%A Wang, Yujun
%A Zhang, Tao
%A Bi, Jinhe
%A Ye, Wenxuan
%A Liu, Boliang
%A Yan, Sikuan
%A Wang, Shuning
%A Zhou, Xuebing
%A Pirk, Soren
%A Schuetze, Hinrich
%A Ma, Yunpu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Aniri
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-memwm
%X World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can still omit task-critical facts, corrupt product attributes, or apply incorrect transition rules. To address such systematic prediction errors, we introduce MemWM, a memory-augmented text-based world model. MemWM uses world memory, a curated memory bank of transition rules, state caches, and hard-to-predict facts, to condition next-state imagination. We evaluate factual state preservation with Structured State Fidelity (SSF), which scores predicted states through benchmark-specific facts and fields. Compared with SFT, memory-augmented training improves SSF by up to 206.3%. In the full planning setting, we keep the policy model frozen and provide policy-side world skill: retrieved task-level skills and step-wise corrective guidance for action selection. Across ALFWorld, WebShop, and ScienceWorld, memory-augmented agents improve downstream success over an SFT-trained world-model agent, with up to a 65.4% relative gain. Sensitivity analyses further show that retrieved memory improves task success and efficiency under different memory and action-budget settings.
%U https://aclanthology.org/2026.emnlp-main.2221/
%P 50542-50564
Markdown (Informal)
[MemWM: Memory-Augmented Text-Based World Model](https://aclanthology.org/2026.emnlp-main.2221/) (Wang et al., EMNLP 2026)
ACL
- Yujun Wang, Tao Zhang, Jinhe Bi, Aniri, Wenxuan Ye, Boliang Liu, Sikuan Yan, Shuning Wang, Xuebing Zhou, Soren Pirk, Hinrich Schuetze, and Yunpu Ma. 2026. MemWM: Memory-Augmented Text-Based World Model. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50542–50564, Budapest, Hungary. Association for Computational Linguistics.