Paper Citation Record · LEDGER

Underspecification Presents Challenges for Credibility in Modern Machine Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2011.03395.

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

pith.paper-citation-record.v1
2011.03395 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 2026-08-07T14:26:34.941345Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

431
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3dbf9447-7d0d-4ac6-90fd-f2d82493a473 · inbound

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects cites this paper.

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:34.941345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44cc8bec-fded-441a-91a3-7bfeebf55772 · inbound

Bias as a Virtue: Rethinking Generalization under Distribution Shifts cites this paper.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:20.859619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f97dfb09-2fc6-40d3-a144-7ddbd9701bf3 · inbound

Machine Learning from Explanations cites this paper.

Machine Learning from Explanations Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T19:45:34.515443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac7d3d50-4077-4eac-af03-ba2c18e903c5 · inbound

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings cites this paper.

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:30:54.271248Z

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.

Observation 30c166a0-cf41-4ee9-a0ea-79c961501dd1 · inbound

Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features cites this paper.

Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 14

Resolution: malformed identifier; arxiv_id, observed 2026-05-09T06:20:41.902028Z

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.

Observation f1a75c7b-a5c9-43ad-b81f-742d5a5a1bd2 · inbound

Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features cites this paper.

Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 14

Resolution: malformed identifier; arxiv_id, observed 2026-05-13T07:02:27.187164Z

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.

Observation 5afa3c22-c07b-41cf-81c7-d38912b9d84c · inbound

Reducing cross-sample prediction churn in scientific machine learning cites this paper.

Reducing cross-sample prediction churn in scientific machine learning Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:17:50.858639Z

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.

Observation d3d1b960-003c-455e-8cef-a8fb8c27f23f · inbound

When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability cites this paper.

When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:19:43.678255Z

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.

Observation 8d859746-924f-4441-96a1-0bf261bbaaa4 · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 121

Resolution: verified exact; arxiv_id, observed 2026-06-28T07:11:45.378289Z

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.

Observation fb496ce3-1f07-4ebe-a8e4-c5d7c32dc49b · inbound

Finding Multiple Interpretations in Datasets cites this paper.

Finding Multiple Interpretations in Datasets Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:17:57.687582Z

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.

Observation dfd22c7c-1565-403d-9ea7-c35dada4d08d · inbound

Collaborative Large and Small Language Models for Accurate and Scalable Data Repair cites this paper.

Collaborative Large and Small Language Models for Accurate and Scalable Data Repair Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:19:04.264397Z

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.

Observation 4bf53128-0cce-45be-8410-0e84849907b6 · inbound

From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems cites this paper.

From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T11:15:42.858675Z

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.