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Paper Citation Record · LEDGER

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

As of 7 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 12 inbound Pith citation observations for arXiv:2507.03971.

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

pith.paper-citation-record.v1
2507.03971 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:02:23.876137Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T06:37:44.328625Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

Reference resolution

21 of 21 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17d8d005-d44d-49d8-82a7-1a793b6090bb · outbound

This paper cites Web data commons - web table corpus 2015 / english-language relational subset,.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Web data commons - web table corpus 2015 / english-language relational subset,

Reference 1

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raw_fallback, observed 2026-08-06T20:02:24.672377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:02:22.237683Z digest=sha256:897b0d8ec13bb8171a51ea838006d257b740d7b77e7db829b77d56ac966c620a

Observation 3b74c694-9d70-4a54-aabc-1fdda53cc4f6 · outbound

This paper cites AMLB: an AutoML Benchmark.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data AMLB: an AutoML Benchmark

Reference 8

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source=pdf_text observed=2026-08-06T20:02:22.715909Z digest=sha256:adeec7551abbdfd8692b1e36b1c6cf0ec1d7f0ceb2608e902c9c2f168c519707

Observation 44c16dea-05a8-4c61-9263-850320855b9e · outbound

This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 11

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Observation de044f67-7e40-4ec5-bf34-b7c96ebfcd95 · outbound

This paper cites doi: 10.1073/pnas.1611835114.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data doi: 10.1073/pnas.1611835114

Reference 15

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source=pdf_text observed=2026-08-06T20:02:23.261687Z digest=sha256:858846941998d71ff62750abe0c1c384973fa9d5fd8b17d4ba4cec271485a75d

Observation ac51047b-7cfd-48f7-8867-c392c8b9c4a5 · outbound

This paper cites an unresolved cited work.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-06T20:02:23.406896Z digest=sha256:40ac67681cf0e353b608f93de0c19fb7e90e8eaddb3b76c9a1e576d32d548fbf

Observation a7aba3c2-448d-4860-bf71-2d8505424f65 · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 18

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source=pdf_text observed=2026-08-06T20:02:23.512645Z digest=sha256:809bb002b2df04078f1cd9b3ad985dcc4cb4957569e6505c20f88340a4947980

Observation 7584a499-e8e8-4750-ab91-5a2ff09e7c1b · outbound

This paper cites TabICL: A Tabular Foundation Model for In-Context Learning on Large Data.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

Reference 19

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source=pdf_text observed=2026-08-06T20:02:23.562165Z digest=sha256:988849295ae62cdf0ee2a8f88a1675c22dd26721bc9cffb3aba27ccf1c443729

Observation f77337be-3b6c-4e19-870b-df14698f79e9 · outbound

This paper cites TabularFM: An Open Framework For Tabular Foundational Models.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data TabularFM: An Open Framework For Tabular Foundational Models

Reference 21

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local_arxiv, observed 2026-08-06T20:02:24.190767Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:02:23.707484Z digest=sha256:3d53904d18915bb2f9fb2a2a35b733ddf9212ae8a5acb6a2a4459383c6477c71

Observation f97ea454-45a6-4016-85b6-b015cfeeacc0 · outbound

This paper cites an unresolved cited work.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Unresolved cited work

Reference 23

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:02:23.876137Z digest=sha256:2f5a14cdaf4470b1800e11cec2cc8f723e426d0c34458f2181827562d7604c32

Observation e145f773-fb1d-41be-b667-56f7f89a7016 · outbound

This paper cites Dorogush, A.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Dorogush, A

Reference 2009

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Observation 0739529b-7ee1-4cf0-aaac-dfe372a75fd5 · outbound

This paper cites URL http://doi.acm.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data URL http://doi.acm

Reference 2013

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source=pdf_text observed=2026-08-06T20:02:23.787071Z digest=sha256:1ae8edb017f9020add28b12a1c2a49681f1bb1af166f895670654b4e4ef71ed2

Observation ef7ed0ad-0cf2-420b-b826-9b67af149960 · outbound

This paper cites XGBoost: A Scalable Tree Boosting System.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data XGBoost: A Scalable Tree Boosting System

Reference 2016

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source=pdf_text observed=2026-08-06T20:02:22.380072Z digest=sha256:1c2eea9b47a6d1fa67d4444cba14591a731449372a8e5f1cc6b7ee74b652c0f5

Observation 8ed1bb63-7304-4a01-8340-f50003c4910d · outbound

This paper cites CatBoost: unbiased boosting with categorical features.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data CatBoost: unbiased boosting with categorical features

Reference 2017

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source=pdf_text observed=2026-08-06T20:02:22.559807Z digest=sha256:b27749b05dfe58c14a1b282a753ec057451d6b8369c366362d619c3b582325c9

Observation 71ccf195-1757-46f5-84f6-5f6bbb6ba282 · outbound

This paper cites Explicit Inductive Bias for Transfer Learning with Convolutional Networks.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Reference 2018

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source=pdf_text observed=2026-08-06T20:02:23.337927Z digest=sha256:551f75ce39f66877d6153c3fdcbc133476a5729c09bfca6268572a8dfbf66fea

Observation c6f8c80a-51c6-40b8-997c-15fcc747e3a6 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 2019

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source=pdf_text observed=2026-08-06T20:02:23.648420Z digest=sha256:fc78f49f635aa066679e8eba4eed9a252743b217708a2563be26bb70884cdf0a

Observation 262858e6-4e90-4ed2-a5c3-ace6a2f3a1d9 · outbound

This paper cites doi: 10.18653/v1/2020.acl-main.740.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data doi: 10.18653/v1/2020.acl-main.740

Reference 2020

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source=pdf_text observed=2026-08-06T20:02:22.851574Z digest=sha256:33b06099011bc986f3eba7b3aca1e74f2cfe0a21e54493ea170599ecc3e23ddc

Observation ce7f499b-7541-4d97-8f97-0c719274fbe3 · outbound

This paper cites GitTables: A Large-Scale Corpus of Relational Tables.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data GitTables: A Large-Scale Corpus of Relational Tables

Reference 2021

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source=pdf_text observed=2026-08-06T20:02:23.108393Z digest=sha256:fa6036e5c4e6ac3633aafb330c24645b37fcaddbf00ff34ce468419d8a312e59

Observation a8643633-b079-4c17-aff6-6fd893889ab1 · outbound

This paper cites Why do tree-based models still outperform deep learning on tabular data?.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Why do tree-based models still outperform deep learning on tabular data?

Reference 2022

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source=pdf_text observed=2026-08-06T20:02:22.768675Z digest=sha256:d01d4828df2c4ff905a58dbc877dc1d66b0baba6752da25da4c2d980af0dbe82

Observation 50725782-dcbc-45d2-bc7d-2b6bf6538d6a · outbound

This paper cites TabLib: A Dataset of 627M Tables with Context.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data TabLib: A Dataset of 627M Tables with Context

Reference 2023

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source=pdf_text observed=2026-08-06T20:02:22.624647Z digest=sha256:76b2b2cbc242f90e5f0d58bc506586991966dc6d54ec8e14ee6524d5fc9f3c96

Observation 367caea1-da5b-4b5a-98a1-cae7a28bf80c · outbound

This paper cites Investigating Data Contamination for Pre-training Language Models.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Investigating Data Contamination for Pre-training Language Models

Reference 2024

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source=pdf_text observed=2026-08-06T20:02:23.208794Z digest=sha256:350f9f10e01f2a34ae8232e9c7ff9b544e83e7c3ad890c18b805fff6959e423c

Observation f3e5aef1-d20f-458e-91af-1e454a441b98 · outbound

This paper cites Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers.

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers

Reference 2025

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source=pdf_text observed=2026-08-06T20:02:22.414784Z digest=sha256:60dae374d64614d8ecc6315ae6ef8106ddc0ccfdaa079258f47f1b8d0dd6c64c

Pith citing papers

Observation 9f6df378-1093-4e43-8498-41b4ed398e6f · inbound

TabArena: A Living Benchmark for Machine Learning on Tabular Data cites this paper.

TabArena: A Living Benchmark for Machine Learning on Tabular Data Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 127

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arxiv_id, observed 2026-05-19T08:42:12.692808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T08:41:35.789878Z digest=sha256:2b90cb75c395494e2657f4f4f104ce58e209fcde8076b4e55b4d61461ec466df

Observation 51db49e4-a95a-4bdd-8866-ee0e5a39063f · inbound

TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models cites this paper.

TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 19

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arxiv_id, observed 2026-05-15T04:14:45.624225Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T04:14:44.792670Z digest=sha256:e92c7d33c241086f5ecb1cd5cadf65dd3991142bcc41d7b9df8ede8dd0efb882

Observation a97a5cd2-291a-4222-affe-94552518b92c · inbound

MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection cites this paper.

MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 23

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arxiv_id, observed 2026-05-16T06:10:40.787911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T06:09:26.402026Z digest=sha256:4696c6f7f2bf6cdcce4b5a302d0c8286e9df6f5926ad56f31c04635f25d1e43f

Observation a7aaf055-b27b-40bb-81d0-7ed890e5e73b · inbound

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data cites this paper.

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 12

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arxiv_id, observed 2026-05-21T10:30:00.280825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-21T10:27:08.416710Z digest=sha256:74aaa1f82981a3de88dd7d0decc1d3cd5fb75b5975721a594c55c95b1e6c7645

Observation 762bb051-0697-4147-a790-d742741ef572 · inbound

KumoRFM-2: Scaling Foundation Models for Relational Learning cites this paper.

KumoRFM-2: Scaling Foundation Models for Relational Learning Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 5

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arxiv_id, observed 2026-05-11T09:46:06.167339Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:50:50.747066Z digest=sha256:16d86e3953e7ad32225309b91ef8c42bc97b2e2dc7a347225148e47e73300d07

Observation 7683e625-09ab-4b0d-a66f-dc620812aab1 · inbound

Tabular foundation models for in-context prediction of molecular properties cites this paper.

Tabular foundation models for in-context prediction of molecular properties Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 43

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arxiv_id, observed 2026-05-10T08:43:01.403841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T08:40:52.116422Z digest=sha256:8281b427e158b7ac1074ce4d1076865de00c502599f6137b184089a336a6446b

Observation 2732aca1-3414-4ca9-8906-f5bbafad8ea0 · inbound

Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning cites this paper.

Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 9

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arxiv_id, observed 2026-05-09T06:25:45.962545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T18:28:19.160555Z digest=sha256:d57d02ce2025622a43cf9c614109fd99f4963a9e61f687697cdbbc5480bd27e7

Observation 5dd484f1-10f5-45f0-8d74-38b19fcb910f · inbound

Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap cites this paper.

Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 9

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arxiv_id, observed 2026-05-20T13:03:18.024407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-20T12:58:37.315496Z digest=sha256:74c353222a577e8f0c5489fa92fbcc9dbff669283c9d70e5a72fa5540b7367ff

Observation d2b4756f-3995-4b0f-b1da-3b6024692507 · inbound

When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach cites this paper.

When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 88

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arxiv_id, observed 2026-05-20T05:43:05.553618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-20T05:42:00.584949Z digest=sha256:cec48e652fc847821d9a99c880d17a6e42d937a223b90411bcb39dcca43b8c06

Observation 8616f651-a6a2-4af7-9d9c-943370f49e98 · inbound

Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos, markerless pose estimation and a tabular foundation model cites this paper.

Simultaneous hyperkinetic movement disorders phenotyping: a cross-cohort pediatric transfer study using routine videos, markerless pose estimation and a tabular foundation model Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 17

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arxiv_id, observed 2026-06-28T02:21:29.285777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T02:11:41.580535Z digest=sha256:695699a9fca76c515fe1897f465d1a9a1ca7b10367b3f6e4fd96fcee2762bb7c

Observation b28e5d77-8a73-4dca-8451-0881ea39618e · inbound

Efficient Adaptive Data Acquisition via Pretrained Belief Representations cites this paper.

Efficient Adaptive Data Acquisition via Pretrained Belief Representations Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 18

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arxiv_id, observed 2026-07-04T17:30:00.755921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-25T23:37:10.555295Z digest=sha256:3db716cad1222476b37df4692d27c4fbfab97a332f65989f7b8e8a026312ecfc

Observation 5051cf63-424b-457e-8076-915eb1a80efd · inbound

Probing Memorization of Tabular In-Context Learning cites this paper.

Probing Memorization of Tabular In-Context Learning Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data

Reference 169

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arxiv_id, observed 2026-07-01T09:25:40.784490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-01T06:37:44.328625Z digest=sha256:3d61354e8e0f296738f500dbd4bf56a8b6e6181a51a73f2b91e70b9fa4bd506d