| Challenge: | Large Language Models can create plans that are neither executable nor verifiable in grounded environments. |
| Approach: | They use Large Language Models to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). |
| Outcome: | The proposed model outperforms the models directly generating plans while being robust to lexical perturbation. |
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Language Model as Planner and Formalizer under Constraints (2026.acl-long)
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| Challenge: | Large language models (LLMs) have been widely used in planning but lack interpretability and control. |
| Approach: | They propose to augment widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories. |
| Outcome: | The proposed model outperforms existing models in 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets. |
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
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| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
| Approach: | They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions. |
| Outcome: | The proposed frameworks are used to support reliable off-the-shelf AP planners. |
Unifying Inference-Time Planning Language Generation (2026.findings-acl)
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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang
| Challenge: | Large language models (LLMs) are used to generate a formal representation of a plan in a planning language. |
| Approach: | They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning. |
| Outcome: | The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages. |
Can LLMs Truly Plan? A Comprehensive Evaluation of Planning Capabilities (2025.findings-emnlp)
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| Challenge: | Existing assessments of planning capabilities of large language models are limited to single-language or specific representation formats. |
| Approach: | a new benchmark is developed to assess the planning capabilities of large language models. |
| Outcome: | The Multi-Plan benchmark highlights performance disparities among models . language differences showed minimal impact, while mathematically structured representations improved accuracy . |
Open Grounded Planning: Challenges and Benchmark Construction (2024.acl-long)
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| Challenge: | Existing work on LLM-based planning uses language generation to produce free-style plans . however, these plans are not grounded in an executable set of actions . |
| Approach: | They propose a new task for open grounded planning that asks the model to generate an executable plan based on a variable action set. |
| Outcome: | The proposed task is open grounded planning, which is based on a set of variables. |
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) struggle to generate correct plans for multi-constraint planning problems . a recent study showed that large language models have significant potential in solving planning problems. |
| Approach: | They propose an LLM-based planning framework that formalizes and solves multi-constraint planning problems as constrained satisfiability problems. |
| Outcome: | The proposed framework achieves a success rate of 93.9% and is effective with diverse paraphrased prompts. |
Creative Planning with Language Models: Practice, Evaluation and Applications (2025.naacl-tutorial)
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| Challenge: | This tutorial explores how planning has been learned and deployed in creative workflows . many human creative tasks involve extensive planning, and actions need to be taken . |
| Approach: | This tutorial explores how planning has been learned and deployed in creative workflows . authors discuss forward and backward learning approaches for planning in LLMs - and evaluation metrics tailored to latent plans . |
| Outcome: | This tutorial examines how planning has been learned and deployed in creative workflows . it discusses forward and backward learning approaches for planning in LLMs - evaluation metrics tailored to latent plans . |
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)
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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo
| Challenge: | Recent studies have encountered limitations in leveraging large language models to generate symbolic world models. |
| Approach: | They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation. |
| Outcome: | The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling. |
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 . |
| Outcome: | The proposed framework analyzes web agents across three layers to improve reasoning, grounding, and recovery. |
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. |
| Approach: | They propose a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes. |
| Outcome: | The proposed framework improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches. |