# Fast and Accurate Task Planning using Neuro-Symbolic Language Models and Multi-level Goal Decomposition

arXiv: https://arxiv.org/abs/2409.19250 · HTML: https://mlsanity.com/paper/2409.19250/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2024.

## Built on by (most cited first, 14 of 14)

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- [CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs](https://mlsanity.com/paper/2509.00971/) (2025)
- [Vision Language Models Cannot Plan, but Can They Formalize?](https://mlsanity.com/paper/2509.21576/) (2025)
- [Egocentric Co-Pilot: Web-Native Smart-Glasses Agents for Assistive Egocentric AI](https://mlsanity.com/paper/2603.01104/) (2026)
- [Understanding Physical Properties of Unseen Deformable Objects by Leveraging Large Language Models and Robot Actions](https://mlsanity.com/paper/2506.03760/) (2025)
- ["Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation](https://mlsanity.com/paper/2506.04500/) (2025)
- [Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation](https://mlsanity.com/paper/2603.06064/) (2026)
- [When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents](https://mlsanity.com/paper/2608.01428/) (2026)
- [ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning](https://mlsanity.com/paper/2605.16309/) (2026)
- [Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda](https://mlsanity.com/paper/2606.13405/) (2026)
- [A Fully Differentiable Neuro-Soft-Symbolic Framework for Perceptual Task Planning](https://mlsanity.com/paper/2609.21221/) (2026)

## Abstract (arXiv)

In robotic task planning, symbolic planners using rule-based representations like PDDL are effective but struggle with long-sequential tasks in complicated environments due to exponentially increasing search space. Meanwhile, LLM-based approaches, which are grounded in artificial neural networks, offer faster inference and commonsense reasoning but suffer from lower success rates. To address the limitations of the current symbolic (slow speed) or LLM-based approaches (low accuracy), we propose a novel neuro-symbolic task planner that decomposes complex tasks into subgoals using LLM and carries out task planning for each subgoal using either symbolic or MCTS-based LLM planners, depending on the subgoal complexity. This decomposition reduces planning time and improves success rates by narrowing the search space and enabling LLMs to focus on more manageable tasks. Our method significantly reduces planning time while maintaining high success rates across task planning domains, as well as real-world and simulated robotics environments. More details are available at http://graphics.ewha.ac.kr/LLMTAMP/.