| Challenge: | Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood . |
| Approach: | They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions . |
| Outcome: | The proposed model achieves 15.6% on a real-world planning benchmark. |
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| Challenge: | Large language models (LLMs) have been widely used in planning but lack interpretability and control. |
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LRPLAN: A Multi-Agent Collaboration of Large Language and Reasoning Models for Planning with Implicit & Explicit Constraints (2025.findings-emnlp)
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| Challenge: | LRPlan is a language-based multi-agent system for complex planning problems . large language models are often unable to maintain consistency across the planning process . |
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