| Challenge: | Existing systems that use pre-trained large language models to perform multi-step logical reasoning have been unable to perform this task. |
| Approach: | They propose a system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure. |
| Outcome: | The proposed system outperforms other competing methods on multiple datasets and significantly outperformed chain-of-thought prompting on the PrOntoQA dataset. |
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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) |
| Outcome: | The proposed framework repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) it achieves optimum balance between exploration and exploitation, while achieving high-reward reasoning paths efficiently. |
A Language-First Approach for Procedure Planning (2023.findings-acl)
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| Challenge: | Developing intelligent agents requires the ability to produce plans on the fly based on visual observations. |
| Approach: | They propose a language-first procedure planning framework with a modularized design . they first align current and goal observations with corresponding steps and then use a pre-trained LM to predict intermediate steps. |
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Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model (2025.acl-long)
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| Challenge: | Existing Chain-of-Thought (CoT) methods struggle with consistency and verification in complex reasoning tasks. |
| Approach: | They propose a framework that integrates structured knowledge representation with learned planning. |
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A Picture is Worth a Thousand Words: Language Models Plan from Pixels (2023.emnlp-main)
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| Challenge: | Recent work uses pre-trained language models to reason about plans from text instructions in embodied visual environments. |
| Approach: | They propose to use pre-trained language models to reason about plan sequences from text instructions in embodied visual environments. |
| Outcome: | The proposed approach outperforms previous approaches on the ALFWorld and VirtualHome benchmarks. |
Ask-before-Plan: Proactive Language Agents for Real-World Planning (2024.findings-emnlp)
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| Challenge: | despite the advancements of large language models, the potential of LLM-powered agents to comprehend ambiguous user instructions is still under exploration. |
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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. |
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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. |
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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. |
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LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers (2023.emnlp-main)
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Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, Roger Levy
| Challenge: | Logical reasoning is an important task for artificial intelligence, says a new study . many prompting-based strategies to enable large language models fail in subtle and unpredictable ways. |
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Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing (2024.emnlp-main)
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| Challenge: | Recent studies have raised concerns regarding the hallucination and flaws in their reasoning process. |
| Approach: | They propose a framework to learn planning-based reasoning through Direct Preference Optimization on collected trajectories, which are ranked according to synthesized process rewards. |
| Outcome: | The proposed model surpasses GPT-3.5-Turbo on logical reasoning benchmarks on a set of logically-based reasoning tasks. |