ClassicLogic: A Knowledge-Driven Benchmark of Classic Puzzle Games for Evaluating Compositional Generalization
arXiv:2607.05185v1 Announce Type: new Abstract: Compositional generalization, the ability to understand and produce novel combinations of known components, remains a fundamental challenge for modern artificial intelligence. While few benchmarks exist, many focus on linguistic tasks and lack complex, explicit compositional structures. We introduce ClassicLogic, a new benchmark suite designed to evaluate an agent's ability to learn and compose problem-solving strategies. The benchmark consists of four classic logic puzzles: Sudoku, KenKen, Kakuro, and Futoshiki. Its core innovation is a hierarch
The continuous push for more robust and generalizable AI models necessitates new benchmarks, especially as current methods fall short in compositional generalization.
This benchmark offers a standardized way to evaluate AI's ability to combine existing knowledge into novel solutions, a crucial step towards more human-like intelligence and autonomous agents.
The introduction of ClassicLogic provides a focused evaluation tool for compositional generalization, potentially accelerating research and development in more capable AI systems.
- · AI researchers
- · AI model developers
- · Autonomous agent developers
- · AI models lacking strong compositional generalization
Researchers gain a new tool to identify strengths and weaknesses in current AI models' ability to combine strategies.
Improved compositional generalization could lead to more robust and versatile AI agents capable of solving complex, novel problems in real-world applications.
Widespread adoption of such benchmarks could accelerate the development of AI systems that require less human oversight and intervention, transforming various industries.
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Read at arXiv cs.AI