2.5-D Decomposition for LLM-Based Spatial Construction

The paper introduces a neuro-symbolic pipeline utilizing 2.5-D decomposition, which enables large language models (LLMs) to plan in a two-dimensional space while a deterministic executor handles vertical placements, significantly reducing systematic coordinate errors in spatial reasoning for autonomous construction. On the Build What I Mean benchmark, the GPT-4o-mini model integrated with this pipeline achieved a mean structural accuracy of 94.6%, outperforming GPT-4o and other competing systems, while demonstrating the ability to run on edge hardware like the Nemotron-3 120B with similar results. This approach is relevant for practitioners as it enhances LLM performance in tasks constrained by physical dimensions, potentially improving reliability in various autonomous construction applications.