Paper Citation Record · LEDGER

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes

As of 16 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2502.02672.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.02672 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:34:30.750467Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:35:42.693764Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T14:35:42.809703Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy12
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c02e25af-5387-436b-a49f-c4e7e4c059dd · outbound

This paper cites Llama 3 model card, 2024.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Llama 3 model card, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.608903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4fda0ad4-1a6b-4bf4-b05e-da92ebea06f3 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Optuna: A next-generation hyperparameter optimization framework

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.610893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea5e04cd-096b-4985-a2df-af6d0b419075 · outbound

This paper cites TabNet: Attentive Interpretable Tabular Learning.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes TabNet: Attentive Interpretable Tabular Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.616236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e2afc2e-b4cd-4ca1-97e6-cf6f890e2044 · outbound

This paper cites Deep neural networks and tabular data: A survey.IEEE Transactions on Neural Networks and Learning Systems, 2022.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Deep neural networks and tabular data: A survey.IEEE Transactions on Neural Networks and Learning Systems, 2022

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.621895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b174647e-8498-4e39-92fd-7e17588aa47c · outbound

This paper cites Hytrel: Hypergraph-enhanced tabular data representation learning.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Hytrel: Hypergraph-enhanced tabular data representation learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.572958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f66efdc3-a9d7-411b-b5f3-8ba3f1f94393 · outbound

This paper cites Xgboost: A scalable tree boosting system.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Xgboost: A scalable tree boosting system

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.631475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3301e58f-36b6-4d3b-b49f-16211c4a0cd2 · outbound

This paper cites Large language models are few (1)-shot table reasoners.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Large language models are few (1)-shot table reasoners

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.444220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 212eebc5-75c9-4bc7-9c43-11b86113061e · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Scaling Instruction-Finetuned Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.641518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9b7a97f-e24d-455b-bd8e-9c28ca6ab0eb · outbound

This paper cites LIFT: Language-interfaced fine-tuning for non-language machine learning tasks.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes LIFT: Language-interfaced fine-tuning for non-language machine learning tasks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.428418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b924a93d-1c72-4133-867d-36f23119bd60 · outbound

This paper cites Uci machine learning repository, 2017.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Uci machine learning repository, 2017

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.412491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 02bfe961-91f8-4179-97b5-f90c82e4c969 · outbound

This paper cites AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.660885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f4c7923-52ca-48bf-ad95-8be704e5a626 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.665403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2bc00b0-ff1e-4060-983d-d5fcbb0309a6 · outbound

This paper cites Friedman.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Friedman

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.669994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb9c0649-0dd0-4262-bf55-8a8a8dede9a6 · outbound

This paper cites Revisiting deep learning models for tabular data.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Revisiting deep learning models for tabular data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.386532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 397550f3-a5ed-44a1-b028-00716e266c44 · outbound

This paper cites an unresolved cited work.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-09T11:34:31.370727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d583b51-bf0d-4fdd-8127-f664e791e64e · outbound

This paper cites Large language models are zero-shot time series forecasters.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Large language models are zero-shot time series forecasters

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.355721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9c7cc9c-df99-4f1b-ae76-fc65c7386416 · outbound

This paper cites Tabllm: Few-shot classification of tabular data with large language models.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Tabllm: Few-shot classification of tabular data with large language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.689257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 499a1660-f764-4edf-8123-1594bfdf2e5b · outbound

This paper cites TabPFN: A transformer that solves small tabular classification problems in a second.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes TabPFN: A transformer that solves small tabular classification problems in a second

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.329174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6497dcb5-7c84-4d73-9a9e-e4b53f586a01 · outbound

This paper cites Large language models for auto- mated data science: Introducing caafe for context-aware automated feature engineering.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Large language models for auto- mated data science: Introducing caafe for context-aware automated feature engineering

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.313383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 950e193c-56f3-4e50-b40a-6e0e1744097b · outbound

This paper cites TABBIE: Pretrained repre- sentations of tabular data.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes TABBIE: Pretrained repre- sentations of tabular data

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.702433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9594595-0c04-44dd-9e05-1bcb24152912 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.706976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8afe2d12-507d-41fd-8f2a-ab57a4906512 · outbound

This paper cites Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.277139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 13d360cc-d1b7-45a6-9ecf-fab729049c64 · outbound

This paper cites When do neural nets outperform boosted trees on tabular data? Advances in Neural Information Processing Systems, 36, 2024.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes When do neural nets outperform boosted trees on tabular data? Advances in Neural Information Processing Systems, 36, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.260867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7a67ed6d-665d-4cb2-999e-102d366e0e1a · outbound

This paper cites Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.720888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef779067-b542-4238-a888-974d49ea8282 · outbound

This paper cites Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.725736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1aed1482-59fb-49b8-90aa-71676cb8f461 · outbound

This paper cites Catboost: unbiased boosting with categorical features.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Catboost: unbiased boosting with categorical features

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.730829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980d07a0-2d85-4714-98ea-837622cdb3c0 · outbound

This paper cites Tablet: Learning from instructions for tabular data.arXiv, 2023.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Tablet: Learning from instructions for tabular data.arXiv, 2023

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:34:31.234661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ae56d11-982f-4f8e-8221-781a2855b5af · outbound

This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.740675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb55f7ea-9d9b-44b7-8078-bf9f9d2288ee · outbound

This paper cites TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T11:34:30.745442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60ce02c9-4119-4d93-ab1f-043e62b88b45 · outbound

This paper cites Qwen2.5: A party of foundation models, September 2024.

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes Qwen2.5: A party of foundation models, September 2024

Reference 31

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-09T11:34:30.969870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 105151e9-5660-430c-a331-b7276de4a1b6 · inbound

FUTURE: Flexible Unlearning for Tree Ensemble cites this paper.

FUTURE: Flexible Unlearning for Tree Ensemble Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:35:42.813135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.