Paper Citation Record · LEDGER

Deep Double Descent: Where Bigger Models and More Data Hurt

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:1912.02292.

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

pith.paper-citation-record.v1
1912.02292 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:35:39.456693Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation af32f0e5-7d0c-4ac6-afd0-117e7699fb20 · inbound

Scaling Laws for Transfer cites this paper.

Scaling Laws for Transfer Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 181

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T00:58:13.588210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5da13e2c-697a-4f42-a34a-23e60159a130 · inbound

A General Language Assistant as a Laboratory for Alignment cites this paper.

A General Language Assistant as a Laboratory for Alignment Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:22:58.815082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa1f059d-5c27-4069-a42b-d03ef31d2081 · inbound

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets cites this paper.

Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:28:53.501806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57a7cee7-a745-4a86-bd6d-5b0361cafc52 · inbound

Scaling Laws and Interpretability of Learning from Repeated Data cites this paper.

Scaling Laws and Interpretability of Learning from Repeated Data Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T15:52:40.468014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 97a498a3-9ecf-4748-9312-636225feef86 · inbound

Language Models (Mostly) Know What They Know cites this paper.

Language Models (Mostly) Know What They Know Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 94

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:42:47.661555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 971d7c18-f602-4a74-b27b-dd9f142e949b · inbound

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders cites this paper.

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:45.771869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa25a18f-a2c7-4a7c-8351-4611a5c002df · inbound

How much do language models memorize? cites this paper.

How much do language models memorize? Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:39.456693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55ef14a2-5aa6-4538-a3da-a5115162bb4c · inbound

Statistical Machine Learning for Astronomy -- A Textbook cites this paper.

Statistical Machine Learning for Astronomy -- A Textbook Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T01:00:28.695469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3bdf3bf-2306-4363-8e0f-10bb4bcc79f5 · inbound

BlueGlass: A Framework for Composite AI Safety cites this paper.

BlueGlass: A Framework for Composite AI Safety Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:20.073892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f0b0817-541d-48ed-8288-c08f29b466a3 · inbound

Detecting AI Assistance in Abstract Complex Tasks cites this paper.

Detecting AI Assistance in Abstract Complex Tasks Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:29:51.719123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 970ae01c-cc50-475c-bf09-adef742c8997 · inbound

Optimizers Qualitatively Alter Solutions And We Should Leverage This cites this paper.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.695553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f749a447-6eb9-497a-8da2-b8f3665a099f · inbound

On Spectral Properties of Gradient-based Explanation Methods cites this paper.

On Spectral Properties of Gradient-based Explanation Methods Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T20:35:47.417880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6e72225-1db8-4709-946e-efde101c09c9 · inbound

Double Descent and Overparameterization in Particle Physics Data cites this paper.

Double Descent and Overparameterization in Particle Physics Data Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T12:40:02.899782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77ed3082-2f62-4fe9-9dca-7d86cf6f5a7a · inbound

Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression cites this paper.

Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 5992

Resolution: unresolved; no resolver link, observed 2026-08-03T03:11:37.476350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f905899-5b01-40d2-b226-12ffebdf80f3 · inbound

Does Order Matter : Connecting The Law of Robustness to Robust Generalization cites this paper.

Does Order Matter : Connecting The Law of Robustness to Robust Generalization Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T21:15:40.396712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96ace875-f50e-4f98-a58d-04cf270a8bde · inbound

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization cites this paper.

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 256

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:49:44.767863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 567e6f6e-da26-4b5c-9321-f2c9d5eb391b · inbound

Position: Ideas Should be the Center of Machine Learning Research cites this paper.

Position: Ideas Should be the Center of Machine Learning Research Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:12:41.079046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d37d9653-898c-4a50-b559-9e371ec50b0a · inbound

Asymmetric Scaling Laws from Sparse Features cites this paper.

Asymmetric Scaling Laws from Sparse Features Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:20:16.955965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5840e183-b397-4edb-8f43-19014c9fa0ef · inbound

Unified Neural Scaling Laws cites this paper.

Unified Neural Scaling Laws Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:44:03.362215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a626ac6-ea5f-4177-924e-cdffa025c620 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-27T21:31:16.825321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b289148b-60bd-4035-8959-3c5fbf6fd51a · inbound

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget cites this paper.

A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget Deep Double Descent: Where Bigger Models and More Data Hurt

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:18:33.989672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.