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    <abstract>Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect immutable environmental constraints. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations—generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation is inherently rigid, struggling to adapt to the open-ended variability of environmental dynamics without continuous, costly retraining. To solve this fundamental misalignment between semantic planning and environmental reality without relying on parameter updates, we introduce WorldMind, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback. Specifically, it unifies Process Experience to enforce semantic physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories. Experiments on EB-ALFRED and EB-Habitat demonstrate that WorldMind achieves the best performance compared to baselines, exhibiting remarkable cross-model and cross-environment transferability.</abstract>
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%0 Conference Proceedings
%T Aligning Agentic World Models via Knowledgeable Experience Learning
%A Ren, Baochang
%A Yao, Yunzhi
%A Sun, Rui
%A Qiao, Shuofei
%A Zhang, Ningyu
%A Chen, Huajun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ren-etal-2026-aligning
%X Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect immutable environmental constraints. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations—generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation is inherently rigid, struggling to adapt to the open-ended variability of environmental dynamics without continuous, costly retraining. To solve this fundamental misalignment between semantic planning and environmental reality without relying on parameter updates, we introduce WorldMind, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback. Specifically, it unifies Process Experience to enforce semantic physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories. Experiments on EB-ALFRED and EB-Habitat demonstrate that WorldMind achieves the best performance compared to baselines, exhibiting remarkable cross-model and cross-environment transferability.
%U https://aclanthology.org/2026.findings-emnlp.668/
%P 14024-14054

Markdown (Informal)

[Aligning Agentic World Models via Knowledgeable Experience Learning](https://aclanthology.org/2026.findings-emnlp.668/) (Ren et al., Findings 2026)

• Aligning Agentic World Models via Knowledgeable Experience Learning (Ren et al., Findings 2026)

ACL

• Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, and Huajun Chen. 2026. Aligning Agentic World Models via Knowledgeable Experience Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 14024–14054, Budapest, Hungary. Association for Computational Linguistics.