This work investigates the reasoning and planning capabilities of foundation models and their scalability in complex, dynamic environments. We introduce PuzzlePlex, a benchmark designed to assess these capabilities through a diverse set of puzzles. PuzzlePlex consists of 15 types of puzzles, including deterministic and stochastic games of varying difficulty, as well as single-player and two-player scenarios. The PuzzlePlex framework provides a comprehensive environment for each game, and supports extensibility to generate more challenging instances as foundation models evolve. Additionally, we implement customized game-playing strategies for comparison. Building on this benchmark, we develop fine-grained metrics to measure performance and conduct an in-depth analysis of frontier foundation models across two settings: instruction-based and code-based. Furthermore, we systematically investigate their scaling limits. Our findings show that reasoning models outperform others in instruction-based settings, while code-based execution presents greater challenges but offers a scalable and efficient alternative. PuzzlePlex enables targeted evaluation and guides future improvements in reasoning, planning, and generalization for foundation models.
@article{arxiv.2510.06475,
title = {PuzzlePlex: Benchmarking Foundation Models on Reasoning and Planning with Puzzles},
author = {Yitao Long and Yuru Jiang and Hongjun Liu and Yilun Zhao and Jingchen Sun and Yiqiu Shen and Chen Zhao and Arman Cohan and Dennis Shasha},
journal= {arXiv preprint arXiv:2510.06475},
year = {2025}
}