Researchers present Minesweeper as a testbed to evaluate the logical reasoning capabilities of Large Language Models (LLMs). They explore LLMs’ potential to understand and execute multi-step logic puzzles beyond their training data.

• Poses Minesweeper as a novel task to challenge LLM reasoning skills.
• Requires understanding the game dynamics, spatial relationships, and logical strategies.
• Analyzes the performance of models, including GPT-4, in puzzle-solving tasks.
• Suggests a fundamental aptitude in LLMs but a struggle with integrated logic processes.
• Calls for more research into reasoning in LLMs to advance AI planning models.

Explore Minesweeper as a tool to probe LLM reasoning. The paper is important for illuminating the gap between current LLMs’ abilities and the requirements for complex reasoning. It encourages the development of models capable of logical deduction and planning to enhance the applicability of LLMs in real-world decision-making contexts.