PuzzleWorld Benchmark Challenges AI Models with 667 Open-Ended Puzzlehunt Problems
A novel benchmark named PuzzleWorld has been launched to assess AI systems on intricate, open-ended reasoning challenges. It consists of 667 puzzlehunt-style questions, which are defined by ambiguous problem statements and necessitate the identification of underlying patterns from multimodal data. This differs from traditional reasoning benchmarks that present tasks with clear guidelines and limited contexts. Each PuzzleWorld puzzle includes a conclusive answer, comprehensive reasoning paths, and cognitive skill tags, facilitating both overall benchmarking and detailed diagnostic evaluation. The goal is to evaluate step-by-step, creative, and multimodal reasoning, reflecting real-life scenarios such as scientific exploration and investigative problem-solving. Despite progress in foundational models, their efficacy in these open-ended tasks remains largely unexamined, with most leading models showing only modest results. The benchmark was revealed on the arXiv preprint server under the identifier arXiv:2506.06211v2, categorized as replace-cross.
Key facts
- PuzzleWorld is a new benchmark for evaluating AI reasoning.
- It contains 667 puzzlehunt-style problems.
- Puzzlehunts lack clear problem definitions and require discovering structure.
- The benchmark assesses step-by-step, open-ended, and creative multimodal reasoning.
- Each puzzle is annotated with a solution, reasoning traces, and skill labels.
- It contrasts with conventional benchmarks with clear instructions.
- The benchmark mirrors real-world domains like scientific discovery.
- Most state-of-the-art AI models achieve limited success on it.
Entities
Institutions
- arXiv