CubeBench: Diagnosing LLM Spatial Reasoning with Rubik's Cube
Published:Dec 29, 2025 09:25
•1 min read
•ArXiv
Analysis
This paper addresses a critical limitation of Large Language Model (LLM) agents: their difficulty in spatial reasoning and long-horizon planning, crucial for physical-world applications. The authors introduce CubeBench, a novel benchmark using the Rubik's Cube to isolate and evaluate these cognitive abilities. The benchmark's three-tiered diagnostic framework allows for a progressive assessment of agent capabilities, from state tracking to active exploration under partial observations. The findings highlight significant weaknesses in existing LLMs, particularly in long-term planning, and provide a framework for diagnosing and addressing these limitations. This work is important because it provides a concrete benchmark and diagnostic tools to improve the physical grounding of LLMs.
Key Takeaways
- •CubeBench is a novel benchmark for evaluating spatial reasoning and long-horizon planning in LLMs.
- •The benchmark uses the Rubik's Cube to create a controlled environment for testing.
- •Experiments revealed significant limitations in existing LLMs, particularly in long-term planning.
- •The paper proposes a diagnostic framework to identify cognitive bottlenecks.
Reference
“Leading LLMs showed a uniform 0.00% pass rate on all long-horizon tasks, exposing a fundamental failure in long-term planning.”