# Towards a General Framework for HTN Modeling with LLMs

arXiv: https://arxiv.org/abs/2511.18165 · HTML: https://mlsanity.com/paper/2511.18165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)](https://mlsanity.com/paper/2401.02500/) (2024)
- [EPO: Hierarchical LLM Agents with Environment Preference Optimization](https://mlsanity.com/paper/2408.16090/) (2024)
- [NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions](https://mlsanity.com/paper/2405.04215/) (2024)
- [OceanPlan: Hierarchical Planning and Replanning for Natural Language AUV Piloting in Large-scale Unexplored Ocean Environments](https://mlsanity.com/paper/2403.15369/) (2024)
- [A Roadmap to Guide the Integration of LLMs in Hierarchical Planning](https://mlsanity.com/paper/2501.08068/) (2025)
- [ChatHTN: Interleaving Approximate (LLM) and Symbolic HTN Planning](https://mlsanity.com/paper/2505.11814/) (2025)
- [STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner](https://mlsanity.com/paper/2506.21030/) (2025)

## Abstract (arXiv)

The use of Large Language Models (LLMs) for generating Automated Planning (AP) models has been widely explored; however, their application to Hierarchical Planning (HP) is still far from reaching the level of sophistication observed in non-hierarchical architectures. In this work, we try to address this gap. We present two main contributions. First, we propose L2HP, an extension of L2P (a library to LLM-driven PDDL models generation) that support HP model generation and follows a design philosophy of generality and extensibility. Second, we apply our framework to perform experiments where we compare the modeling capabilities of LLMs for AP and HP. On the PlanBench dataset, results show that parsing success is limited but comparable in both settings (around 36%), while syntactic validity is substantially lower in the hierarchical case (1% vs. 20% of instances). These findings underscore the unique challenges HP presents for LLMs, highlighting the need for further research to improve the quality of generated HP models.