SymboUQ: A New Framework for Symbolic Uncertainty Quantification in LLM Spatial Reasoning

A novel framework named SymboUQ has been developed to tackle the issue of assessing the reliability of final outputs in large language models (LLMs) during spatial reasoning tasks. This framework, outlined in a paper on arXiv (ID: 2608.00417), differentiates between symbolizability—indicating if a statement can be expressed in a formal verifier's language—and semantic determinacy, which assesses if executing that statement results in a clear verdict or an ambiguous outcome. SymboUQ includes two primary elements: a Layout Auditor that processes ordered spatial claims to gather evidence on feasibility, conflict, and necessary repairs, and a label-free Deter, likely a determinacy estimator. The goal of SymboUQ is to provide a more reliable method for quantifying uncertainty in spatial reasoning, addressing the limitations of token-level confidence in LLMs. The full paper can be accessed at https://arxiv.org/abs/2608.00417.