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

Computer Science > Artificial Intelligence [Submitted on 15 Apr 2026] ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold Chenlang Yi, Gang Li, Zizhan Xiong, Tue Minh Cao, Yanmin Gong, My T. Thai, Tianbao Yang 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. To address the dual challenges of scalable data curation and reasoning consistency, we propose ReSS, a systematic framework that bridges symbolic and neural reasoning models. ReSS leverages a decision-tree model to extract instance-level decision paths as symbolic scaffolds. These scaffolds, alongside input features and labels, guide an LLM to generate grounded natural-language reasoning that strictly adheres to the underlying decision logic. The resulting high-quality dataset is used to fine-tune a pretrained LLM into a specialized tabular reasoning model, further enhanced by a scaffold-invariant data augmentation strategy to improve generalization and explainability. To rigorously assess faithfulness, we introduce quantitative metrics including hallucination rate, explanation necessity, and explanation sufficiency. Experimental results on medical and financial benchmarks demonstrate that ReSS-trained models improve traditional decision trees and standard fine-tuning approaches up to 10\% while producing faithful and consistent reasoning Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.13392 [cs.AI]   (or arXiv:2604.13392v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.13392 Focus to learn more Submission history From: Chenlang Yi [view email] [v1] Wed, 15 Apr 2026 01:43:00 UTC (2,364 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)