ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold

ArXi:2604.13392v1 Announce Type: new Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy and faithful, human-understandable reasoning. While symbolic models offer verifiable logic, they lack semantic expressiveness. Meanwhile, general-purpose LLMs often require specialized fine-tuning to master domain-specific tabular reasoning.