AI RESEARCH
2.5-D Decomposition for LLM-Based Spatial Construction
arXiv CS.AI
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ArXi:2605.07066v1 Announce Type: new Autonomous systems that build structures from natural-language instructions need reliable spatial reasoning, yet large language models (LLMs) make systematic coordinate errors when generating three-dimensional block placements. We present a neuro-symbolic pipeline based on \emph{2.5-D decomposition}: the LLM plans in the two-dimensional horizontal plane while a deterministic executor computes all vertical placement from column occupancy, eliminating an entire class of errors.