HyPA-MAPF-Benchmark

Dataset supporting the paper “Validation-Driven Assessment of Large Language Models and HyPA-MAPF for Multi-Agent Path Finding in Constrained Industrial Scenarios”.

This dataset contains the benchmark instances, prompts, model outputs, validation results, selected solver information, makespan values, and generated paths used in the experimental evaluation. The benchmark includes 100 grid-based Multi-Agent Path Finding scenarios under the disappear-at-goal convention, divided into five categories: simple no-conflict, open-space crossing, narrow corridor crossing, side-pocket required, and three-robot bottleneck.

The dataset includes results for Independent A-star, Sequential Priority A-star, the proposed HyPA-MAPF architecture, and three Large Language Models: Gemini, Claude, and ChatGPT. For each scenario, the dataset provides the prompt given to the LLMs, the raw generated paths, the validated solution paths for the algorithmic methods, success or failure indicators, makespan values, and the selected HyPA-MAPF solver.

The material is intended to support reproducibility, independent validation, and further research on validation-driven assessment of LLMs and hybrid planning methods for Multi-Agent Path Finding.