Puzzle Solving

Puzzle solving, encompassing diverse problem types from logic puzzles to image reconstruction, is a key area of research exploring artificial intelligence's reasoning and problem-solving capabilities. Current research focuses on evaluating and improving large language models (LLMs) and other neural architectures, such as diffusion models and Monte Carlo Tree Search, for solving various puzzle types, often employing techniques like chain-of-thought prompting and constraint satisfaction. These efforts aim to understand the limitations of current AI in complex reasoning and to develop more robust and efficient algorithms for solving puzzles, with implications for broader applications in areas like computer vision, natural language processing, and automated game design. The development of new benchmark datasets and evaluation metrics is also a significant focus, enabling more rigorous comparisons of different approaches.