Pith Number

pith:3DKCT2U7

pith:2026:3DKCT2U7UEJAJBOXXWRK4WPQOB

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

Chenlang Yi, Gang Li, My T. Thai, Tianbao Yang, Tue Minh Cao, Yanmin Gong, Zizhan Xiong

ReSS extracts decision paths from trees to scaffold LLM fine-tuning for tabular prediction with faithful reasoning.

arxiv:2604.13392 v2 · 2026-04-15 · cs.AI

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Claims

C1strongest claim

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.

C2weakest assumption

That decision-tree paths extracted from the data can serve as sufficient and non-restrictive scaffolds that force an LLM to generate reasoning which is both logically faithful and semantically useful without introducing new inconsistencies or losing predictive power.

C3one line summary

ReSS uses decision-tree scaffolds to fine-tune LLMs for faithful tabular reasoning, reporting up to 10% gains over baselines on medical and financial data.

Receipt and verification

First computed	2026-05-20T01:05:13.307052Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d8d429ea9fa1120485d7bda2ae59f0707c43533db2a1784541da174baae2f9d1

Aliases

arxiv: 2604.13392 · arxiv_version: 2604.13392v2 · doi: 10.48550/arxiv.2604.13392 · pith_short_12: 3DKCT2U7UEJA · pith_short_16: 3DKCT2U7UEJAJBOX · pith_short_8: 3DKCT2U7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3DKCT2U7UEJAJBOXXWRK4WPQOB \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d8d429ea9fa1120485d7bda2ae59f0707c43533db2a1784541da174baae2f9d1

Canonical record JSON

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    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-04-15T01:43:00Z",
    "title_canon_sha256": "0b3db06d23ee531df9516d0b7662208bc175dd40edda1dfad0e6e7281fb92742"
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