PuzzlePlex: Benchmarking Foundation Models on Reasoning and Planning
  with Puzzles
Authors: name: Yitao Long, name: Yuru Jiang, name: Hongjun Liu, name: Yilun Zhao, name: Jingchen Sun, name: Yiqiu Shen, name: Chen Zhao, name: Arman Cohan, name: Dennis Shasha

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.