Paper Citation Record · LEDGER

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms

As of 4 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2604.04868.

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

pith.paper-citation-record.v1
2604.04868 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T20:02:14.838067Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:37:00.829978Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact8
verified fuzzy22
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8059f596-8e12-4d7d-a364-18c24e100bef · outbound

This paper cites Tabnet: Attentive interpretable tabular learning.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Tabnet: Attentive interpretable tabular learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.035266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c521b06e-7381-434a-ac55-8d9016c78807 · outbound

This paper cites Orion-MSP: Multi-scale sparse attention for tabular in-context learning.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Orion-MSP: Multi-scale sparse attention for tabular in-context learning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:51.318764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 213862f4-bd01-40d4-8e87-2bafa875c81d · outbound

This paper cites Random forests.Machine learning, 45(1):5–32.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Random forests.Machine learning, 45(1):5–32

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.044799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fcad30aa-24ab-42e7-921a-69b3e6fc7142 · outbound

This paper cites Xgboost: A scalable tree boosting system.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Xgboost: A scalable tree boosting system

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.053788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 49d22878-79e2-4dda-95c6-f6341a92dfd0 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in neural information processing systems, 35:16344–16359.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in neural information processing systems, 35:16344–16359

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.049067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0fc510d3-ea33-4bdf-b6cb-ad9ca98e1adf · outbound

This paper cites A survey on in-context learning.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms A survey on in-context learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.040032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 47e91c5f-6478-42ee-a37c-da31d4b43881 · outbound

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

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms TabArena: A Living Benchmark for Machine Learning on Tabular Data

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:51.324862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 43a4e98e-95cc-4a76-8508-dfaaae341713 · outbound

This paper cites Do we need hundreds of classifiers to solve real world classification problems?The journal of machine learning research, 15(1):3133–3181.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Do we need hundreds of classifiers to solve real world classification problems?The journal of machine learning research, 15(1):3133–3181

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.967698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 397a2e0b-63c8-4d93-92b9-47bbdb5bae33 · outbound

This paper cites Revisiting deep learning models for tabular data.Advances in neural information processing systems, 34:18932– 18943.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Revisiting deep learning models for tabular data.Advances in neural information processing systems, 34:18932– 18943

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.003574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fd3b9f7e-f188-4a27-acb8-b717b96859dc · outbound

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

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:14:46.340333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fa49a96a-1537-4f36-83ed-5436cc3039d9 · outbound

This paper cites Why do tree-based models still out- perform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Why do tree-based models still out- perform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.972341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation daee62ce-ca79-49ec-863b-bcba35817982 · outbound

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

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:04:00.647068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39def567-3d95-4960-a29d-3ee62c6812cb · outbound

This paper cites Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Accurate predictions on small data with a tabular foundation model.Nature, 637(8045):319–326

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.981971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e2dbafb7-6d12-48f1-a5e1-af3e0ab558a0 · outbound

This paper cites Well-tuned simple nets excel on tabular datasets.Advances in neural information processing systems, 34:23928–23941.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Well-tuned simple nets excel on tabular datasets.Advances in neural information processing systems, 34:23928–23941

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.963195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 829a547e-65c4-4fef-992c-8eb54c29034a · outbound

This paper cites Robustness of random forest-based gene selection methods.BMC bioinformatics, 15(1):8.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Robustness of random forest-based gene selection methods.BMC bioinformatics, 15(1):8

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.018688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 744fa608-482e-431d-8d9d-d363cb04f288 · outbound

This paper cites A unified approach to interpreting model predictions.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms A unified approach to interpreting model predictions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.997652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fecce517-0910-42b1-b8dd-527dac8b93ae · outbound

This paper cites TabDPT: Scaling tabular foundation models on real data.arXiv preprint arXiv:2410.18164.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms TabDPT: Scaling tabular foundation models on real data.arXiv preprint arXiv:2410.18164

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:51.292637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4b79eb66-5c13-45e0-bbd4-97eb4e0ce924 · outbound

This paper cites s1: Simple test-time scaling.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms s1: Simple test-time scaling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.992308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c6817de1-378f-40e0-98eb-08eed82a1f15 · outbound

This paper cites Assessing the robustness of tabular prior-data fitted network classifier.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Assessing the robustness of tabular prior-data fitted network classifier

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.952450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c92cc826-f4d9-41e1-99c9-39b1bce3aa78 · outbound

This paper cites Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.957730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 40e28c3e-1499-47cf-9857-d7d48226d918 · outbound

This paper cites arXiv preprint arXiv:2511.07236 , year=.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms arXiv preprint arXiv:2511.07236 , year=

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:51.302455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f88bdd12-f65b-459f-a925-c5fc65e10eb3 · outbound

This paper cites Exploring fine-tuning for tabular foundation models.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Exploring fine-tuning for tabular foundation models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:51.298282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 25ffec71-7121-4138-8a49-ba43735aa25c · outbound

This paper cites Why Tabular Foundation Models Should Be a Research Priority.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Why Tabular Foundation Models Should Be a Research Priority

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:51.307235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation caca995a-2934-4302-8709-c7992be33f73 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Attention is all you need.Advances in neural information processing systems, 30

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.008898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 23f2ef2e-8259-46f9-82f4-0bde0e450367 · outbound

This paper cites Transformers learn in-context by gra- dient descent.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Transformers learn in-context by gra- dient descent

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.023292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2d625eda-e34f-472f-9bc9-44d006717bcb · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Chain-of-thought prompting elicits reasoning in large language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.987416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aea46d29-cef7-4d41-a1e8-477424d2c404 · outbound

This paper cites A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:15:51.313946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 32fc8470-a732-4a40-b54f-30414617341c · outbound

This paper cites Furthermore, to eliminate any artifacts from ensembling and randomness, we use 1 estimator and disable feature shuffling when fitting the model.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms Furthermore, to eliminate any artifacts from ensembling and randomness, we use 1 estimator and disable feature shuffling when fitting the model

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.028305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 40a46135-0529-43d6-9d7d-ac0b66d893df · outbound

This paper cites This changes the random realization of the dataset, varying the relationship between informative features and class labels.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms This changes the random realization of the dataset, varying the relationship between informative features and class labels

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:11.977429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d1a1e425-06c4-4c60-88aa-4b41bce198b3 · outbound

This paper cites This introduces multimodality within each class, making the class structure more complex and the decision boundary more nonlinear.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms This introduces multimodality within each class, making the class structure more complex and the decision boundary more nonlinear

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.031600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e02def79-f4a0-4e92-86d3-3cca9fe35296 · outbound

This paper cites This reduces the separation between classes, weakening the relationship between informative features and class labels.

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms This reduces the separation between classes, weakening the relationship between informative features and class labels

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T03:02:12.014124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 15085fae-c4b2-4da0-9f61-3e8becf40424 · inbound

Topological Signatures of Context-Level Reliability in TabPFN cites this paper.

Topological Signatures of Context-Level Reliability in TabPFN Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T16:37:00.829978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.