Paper Citation Record · LEDGER

Limitations of the NTK for Understanding Generalization in Deep Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2206.10012.

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

pith.paper-citation-record.v1
2206.10012 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:19:07.021901Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:47:10.307161Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 8da687c9-9cfd-4ea6-a967-0d71f2b75040 · inbound

Adaptive kernel predictors from feature-learning infinite limits of neural networks cites this paper.

Adaptive kernel predictors from feature-learning infinite limits of neural networks Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T11:19:07.021901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bdd98f0-9d2a-4d3b-956b-480b6a9697d8 · inbound

Feature learning is decoupled from generalization in high capacity neural networks cites this paper.

Feature learning is decoupled from generalization in high capacity neural networks Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T14:17:37.234144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f7a2778-c414-4f50-b860-aaa24c6be19e · inbound

Harnessing Optimization Dynamics for Curvature-Informed Model Merging cites this paper.

Harnessing Optimization Dynamics for Curvature-Informed Model Merging Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T17:03:17.184315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e20161cf-43e6-4be2-8c21-a002f48ac39d · inbound

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning cites this paper.

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-02T21:30:53.974565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 848d2f75-143d-4b81-90f0-6f18cd14dd81 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 183

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:21:08.617679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 79b371c7-0cbf-4893-a2ac-e4e881bb6e05 · inbound

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer cites this paper.

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:05:53.374387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b1d58a01-2497-415d-a906-e8cc58f9cd1c · inbound

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer cites this paper.

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:26:24.239292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 491e39fc-3f96-4bf2-afbd-279dc765d3c5 · 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 Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:49:44.889107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ce7d5c1a-edbc-48fd-b6e5-c14272b2710a · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 232

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:32:56.168233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d3f3449-c7c6-43a3-84f0-6ea06f6ddc78 · inbound

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 232

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:40:24.900864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation daa0ee55-38c7-4c2f-a657-a1f7e9b7c271 · inbound

Pointwise Generalization in Deep Neural Networks cites this paper.

Pointwise Generalization in Deep Neural Networks Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:38:16.908806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2909d9f0-f5df-40dd-bb3f-6f48530048b9 · inbound

Second-Order Path Kernel Interpolation Formulas in Machine Learning cites this paper.

Second-Order Path Kernel Interpolation Formulas in Machine Learning Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:10.308432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c89ad082-364c-452e-878e-88727cd65d5f · inbound

The Cost of Discretization in Functional Linear Regression: Minimax Rates and Adaptation cites this paper.

The Cost of Discretization in Functional Linear Regression: Minimax Rates and Adaptation Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 119

Resolution: unresolved; no resolver link, observed 2026-07-13T03:41:36.654380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8bc3caf-7b71-4841-ac08-c92468b05caf · inbound

A Defense of the Quadratic Model cites this paper.

A Defense of the Quadratic Model Limitations of the NTK for Understanding Generalization in Deep Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T06:58:10.426306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.