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

ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-16
Score updated: 2026-04-16
Score fresh until: 2026-05-16
References: 0
Source count: 3
Coverage: 50%

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

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

PDF: https://arxiv.org/pdf/2604.13392v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-16T18:18:38.672Z

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

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Symbolic Scaffold Chenlang Yi * 1 Gang Li * 1 Zizhan Xiong 1 Tue Minh Cao2 Yanmin Gong1 My T. Thai2 Tianbao Yang1 Abstract Tabular data remains prevalent in high-stakes do- mains such as healthcare and finance
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a sequence of tokens. 3.3. Symbolic Scaffold Informed Reasoning Dataset Curation Given a symbolic scaffold that specifies the constraints of the decision process, we leverage the input features, the output label
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shrink the size of training data for SFT. Below, we present an effective data augmentation strategy. A data augmentation is usually performed by perturbing the input features. However
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Creditg 20 800 641 2564 100 100 Diabetes 8 614 536 2144 77 77 HomeLoan 11 491 406 1624 61 62 reasoning traces by setting their values to unknown in the input, and measure the resulting performance degradation. Similarly
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mann et al., 2023) for the Diabetes and Creditg datasets. For the AD and HomeLoan datasets, we design dataset-specific serialization templates, with details provided in Appendix D. Baselines
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base LLM, corresponding to the approach in (Xu et al., 2025). For all LLM fine-tuning methods, we use Qwen-2.5- 3B-Instruct as the base model. For RL, we use the recently proposed DisCO algorithm (Li et al., 2025)
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not invent non-existent features or incorrect feature values in its reasoning. Comparison Hallucination occurs only rarely, with rates below 2% on all datasets
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generic architectures such as MLPs, which lack induc- tive biases aligned with the structure of tabular decision manifolds and often struggle to match the performance of tree-based methods (Arik & Pfister, 2020)
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ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold

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