# Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning

arXiv: https://arxiv.org/abs/2311.13720 · HTML: https://mlsanity.com/paper/2311.13720/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 6 of its 74 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 6 of 6)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [Plansformer: Generating Symbolic Plans using Transformers](https://mlsanity.com/paper/2212.08681/) (2022)
- [Explainable Planning](https://mlsanity.com/paper/1709.10256/) (2017)
- [MACQ: A Holistic View of Model Acquisition Techniques](https://mlsanity.com/paper/2206.06530/) (2022)
- [Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning](https://mlsanity.com/paper/2112.01918/) (2021)

## Built on by (most cited first, 8 of 8)

- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [Evaluating the Goal-Directedness of Large Language Models](https://mlsanity.com/paper/2504.11844/) (2025)
- [SPEAR: Security Posture Evaluation using AI Planner-Reasoning on Attack-Connectivity Hypergraphs](https://mlsanity.com/paper/2506.01227/) (2025)
- [Model Space Reasoning as Search in Feedback Space for Planning Domain Generation](https://mlsanity.com/paper/2604.08712/) (2026)
- [Planning in the LLM Era: Building for Reliability and Efficiency](https://mlsanity.com/paper/2605.21902/) (2026)
- [Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration](https://mlsanity.com/paper/2311.18355/) (2023)
- [Model-Based AI planning and Execution Systems for Robotics](https://mlsanity.com/paper/2505.04493/) (2025)
- [MMP-A*: Multimodal Perception Enhanced Incremental Heuristic Search on Path Planning](https://mlsanity.com/paper/2601.01910/) (2026)

## Abstract (arXiv)

This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks. To set the stage for this union, we explore two different flavors of model space problems that have been studied in the AI planning literature and explore the effect of an LLM on those tasks. We empirically demonstrate how the performance of an LLM contrasts with combinatorial search (CS) -- an approach that has been traditionally used to solve model space tasks in planning, both with the LLM in the role of a standalone model space reasoner as well as in the role of a statistical signal in concert with the CS approach as part of a two-stage process. Our experiments show promising results suggesting further forays of LLMs into the exciting world of model space reasoning for planning tasks in the future.