Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA

This paper challenges the common belief that temporal reasoning is the primary limitation of large language models (LLMs) in complex tasks. Instead, it argues that the real issue stems from unstructured text-to-event representation. The authors propose a neuro-symbolic question-answering framework that utilizes a Probabilistic Inconsistency Signal (PIS) to differentiate between perceptual errors and reasoning failures, enhancing the model's ability to handle temporal reasoning through structured event graphs.