# GenePlan: Evolving Better Generalized PDDL Plans using Large Language Models

arXiv: https://arxiv.org/abs/2603.09481 · HTML: https://mlsanity.com/paper/2603.09481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Property-Guided LLM Program Synthesis for Planning](https://mlsanity.com/paper/2605.16142/) (2026)

## Abstract (arXiv)

We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate domain-dependent generalized planners for classical planning tasks described in PDDL. By casting generalized planning as an optimization problem, GenePlan iteratively evolves interpretable Python planners that minimize plan length across diverse problem instances. In empirical evaluation across six existing benchmark domains and two new domains, GenePlan achieved an average SAT score of 0.91, closely matching the performance of the state-of-the-art planners (SAT score 0.93), and significantly outperforming other LLM-based baselines such as chain-of-thought (CoT) prompting (average SAT score 0.64). The generated planners solve new instances rapidly (average 0.49 seconds per task) and at low cost (average 1.82 per domain using GPT-4o).