Deconstructing Spatial Complexity: Hierarchical Decomposition for LLM Spatial Reasoning
LLMs have shown remarkable proficiency in general language understanding and reasoning. However, they consistently underperform in spatial reasoning that severely limits their application, particularly in embodied intelligence. Inspired by the success of hierarchical reinforcement learning, this paper introduces a novel method for hierarchical task decomposition in LLM spatial reasoning. Our approach guides LLMs to decompose complex tasks into manageable sub-tasks by identifying key intermediate states and generating simplified sub-environments. However, we identify that LLMs often fail to derive optimal intermediate states due to their insufficient spatial prior, leading to sub-optimal t...
| Authors | Yi Wang, Haojie Lu, Zhaofan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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