From Symbolic to Geometric: Enabling Spatial Reasoning in Large Language Models

ArXi:2606.04381v1 Announce Type: cross Recent large language models (LLMs) often appear to exhibit spatial reasoning ability; however, this capability is largely \emph{symbolic}, arising from pattern matching over spatial language rather than true \emph{geometric} reasoning over space. Because LLMs operate on discrete tokens, they lack native for continuous spatial representations, explicit geometric computation, and structured spatial operators. To address this limitation, we Our instruction dataset, evaluation benchmark, model.