2.5-D Decomposition for LLM-Based Spatial Construction
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 2.5-D decomposition: the LLM plans in the two-dimensional horizontal plane while a deterministic executor computes all vertical placements from column occupancy, eliminating an entire class of errors. On the Build What I Mean benchmark (160 rounds), GPT-4o-mini with this pipeline achieves 94.6% mean structural accuracy across 12 independent runs, within 3.0 percentage points of the 97.6% ceiling imposed by architect-age...
| Authors | Paul Whitten, Li-Jen Chen, Sharath Baddam |
|---|---|
| Primary category | cs.AI |
| Published | 8 May 2026 |
| Last updated at source | 11 July 2026 |
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