Paper Citation Record · LEDGER

On the Uncertainty Quantification Ability of Tabular Foundation Models

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2606.01427.

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

pith.paper-citation-record.v1
2606.01427 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T15:59:54.471535Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact2
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a128e17-75b2-492b-90b6-e42e6b6646f0 · outbound

This paper cites Weight uncertainty in neural network.

On the Uncertainty Quantification Ability of Tabular Foundation Models Weight uncertainty in neural network

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f283f92-2fdb-4c07-b8c5-2d8aaed6dac2 · outbound

This paper cites The MIT Press, 2006.

On the Uncertainty Quantification Ability of Tabular Foundation Models The MIT Press, 2006

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e70e49f-69ce-4c5f-ab66-493b870dec7e · outbound

This paper cites Physical systems with random uncertainties: Chaos represen- tations with arbitrary probability measure.SIAM Journal on Scientific Computing, 26(2):395–410, 2004.

On the Uncertainty Quantification Ability of Tabular Foundation Models Physical systems with random uncertainties: Chaos represen- tations with arbitrary probability measure.SIAM Journal on Scientific Computing, 26(2):395–410, 2004

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13768996-1051-4278-bfaf-d0eaa67bea61 · outbound

This paper cites Srivastava, G.

On the Uncertainty Quantification Ability of Tabular Foundation Models Srivastava, G

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90543808-d019-4773-a223-94b66e161b93 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncer- tainty in deep learning.

On the Uncertainty Quantification Ability of Tabular Foundation Models Dropout as a bayesian approximation: Representing model uncer- tainty in deep learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb4ae4e1-d0ac-42e8-9509-322bbbddea32 · outbound

This paper cites A survey of transformers, 2021.

On the Uncertainty Quantification Ability of Tabular Foundation Models A survey of transformers, 2021

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 193a3780-b54f-4c92-a2e5-f2939fca3dcd · outbound

This paper cites Accurate predictions on small data with a tabular foun- dation model.Nature, 637:319–326, 2025.

On the Uncertainty Quantification Ability of Tabular Foundation Models Accurate predictions on small data with a tabular foun- dation model.Nature, 637:319–326, 2025

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c83f55b5-ab2f-4062-9021-5594b6dc4192 · outbound

This paper cites Gp+: a python library for kernel-based learning via gaussian processes.Advances in Engineering Software, 195:103686, 2024.

On the Uncertainty Quantification Ability of Tabular Foundation Models Gp+: a python library for kernel-based learning via gaussian processes.Advances in Engineering Software, 195:103686, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a78bbf66-6628-49ff-8d27-a63b7a664427 · outbound

This paper cites an unresolved cited work.

On the Uncertainty Quantification Ability of Tabular Foundation Models Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfe15052-1220-45a8-a0f5-2620684da56b · outbound

This paper cites Classes of kernels for machine learning: a statistics perspective.Journal of machine learning research, 2(Dec):299–312, 2001.

On the Uncertainty Quantification Ability of Tabular Foundation Models Classes of kernels for machine learning: a statistics perspective.Journal of machine learning research, 2(Dec):299–312, 2001

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90645f40-3977-4bc2-a1c3-41f52c1876f3 · outbound

This paper cites Sparse gaussian processes using pseudo-inputs.

On the Uncertainty Quantification Ability of Tabular Foundation Models Sparse gaussian processes using pseudo-inputs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 692307ab-2303-4e34-b008-ffcda15e39b2 · outbound

This paper cites Variable noise and dimensionality reduction for sparse Gaussian processes.

On the Uncertainty Quantification Ability of Tabular Foundation Models Variable noise and dimensionality reduction for sparse Gaussian processes

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-01T21:56:15.850060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c47746a4-16ce-4386-8803-00d8fe8be70c · outbound

This paper cites Gaussian Processes for Big Data.

On the Uncertainty Quantification Ability of Tabular Foundation Models Gaussian Processes for Big Data

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-01T21:56:15.847690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f7b973f3-2f46-424f-a569-c5a9e8b21478 · outbound

This paper cites Distributed gaussian processes.

On the Uncertainty Quantification Ability of Tabular Foundation Models Distributed gaussian processes

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c671ec9-42c8-42f2-a68d-d1b7a7db0e42 · outbound

This paper cites Scalable variational gaussian process classification.

On the Uncertainty Quantification Ability of Tabular Foundation Models Scalable variational gaussian process classification

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd0723b-e7ca-4ad4-8893-4e54cc47bc0d · outbound

This paper cites Deep kernel learning.

On the Uncertainty Quantification Ability of Tabular Foundation Models Deep kernel learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38c208e3-0b38-440a-b5ba-aa8e8ca31ba5 · outbound

This paper cites Surjanovic and D.

On the Uncertainty Quantification Ability of Tabular Foundation Models Surjanovic and D

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 017f1501-0bc1-4b7b-9a83-593482f11143 · outbound

This paper cites Latent map gaussian processes for mixed variable metamod- eling.Computer Methods in Applied Mechanics and Engineering, 387:114128, 2021.

On the Uncertainty Quantification Ability of Tabular Foundation Models Latent map gaussian processes for mixed variable metamod- eling.Computer Methods in Applied Mechanics and Engineering, 387:114128, 2021

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf550d44-b872-42c4-90a1-fa2a6910b3cf · outbound

This paper cites Non-stationary kernel learning in gaussian processes.Journal of Mechanical Design, 148(2):021714, 2026.

On the Uncertainty Quantification Ability of Tabular Foundation Models Non-stationary kernel learning in gaussian processes.Journal of Mechanical Design, 148(2):021714, 2026

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c5de975-f514-4892-a3ac-9a08a8cb2201 · outbound

This paper cites Random features for large-scale kernel machines.Advances in neural information processing systems, 20, 2007.

On the Uncertainty Quantification Ability of Tabular Foundation Models Random features for large-scale kernel machines.Advances in neural information processing systems, 20, 2007

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1bd5909-1daf-47b7-abbc-1c60ad50c444 · outbound

This paper cites A survey on high-dimensional gaussian process modeling with application to bayesian optimization.ACM Transactions on Evolutionary Learning and Optimization, 2(2):1–26, 2022.

On the Uncertainty Quantification Ability of Tabular Foundation Models A survey on high-dimensional gaussian process modeling with application to bayesian optimization.ACM Transactions on Evolutionary Learning and Optimization, 2(2):1–26, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T15:59:54.471535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.