| Challenge: | Existing LLM agents fail to assimilate new observations into dynamic updates of the world model, leading to divergent and erroneous plans. |
| Approach: | They propose a hierarchical agent architecture that allows LLM planning to co-evolve with a dynamically updated model of the world. |
| Outcome: | The proposed agent outperforms existing agent paradigms in planning and exploration. |
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Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma
| Challenge: | Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks . |
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Enhancing Agent Learning through World Dynamics Modeling (2024.findings-emnlp)
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| Challenge: | Existing methods assume that large language models have a complete understanding of their environment, overlooking potential gaps in their grasp of actual world dynamics. |
| Approach: | They propose a framework that discovers world dynamics from a small number of demonstrations, verifies the correctness of these dynamics, and evolves new, advanced dynamics tailored to the current situation. |
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CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)
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| Challenge: | Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively. |
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SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)
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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
| Challenge: | Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks. |
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GraphMind: LLMs as Dynamic Knowledge Builders for Sequential Decision-Making (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance in natural language understanding and generation, establishing themselves as foundational tools across a wide range of domains. |
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Iterative Formalization and Planning in Partially Observable Environments (2026.findings-acl)
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| Challenge: | Existing methods to formalize an environment into the Planning Domain Definition Language (PDDL) have been shown to improve performance and control. |
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Model-Based Imaginative Planning for Embodied Agents (2026.acl-long)
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Junru Song, Hengzhe Jin, Yucong Huang, Tingsong Jiang, Weien Zhou, Feifei Wang, Yang Yang, Ying Wen, Wen Yao
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Reasoning with Language Model is Planning with World Model (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. |
| Approach: | They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) |
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EPO: Hierarchical LLM Agents with Environment Preference Optimization (2024.emnlp-main)
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| Challenge: | Long-horizon decision-making tasks require extensive planning over multiple steps, maintaining coherence and goal orientation, which is difficult for LLMs that are typically designed for more immediate and localized predictions. |
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Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective (2026.acl-long)
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| Challenge: | Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. |
| Approach: | They propose a hierarchical planning framework that analyzes web agents across three layers . they show that structured Planning Domain Definition Language (PDDL) plans produce more concise and goal-directed strategies than natural language (NL) plans . |
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