Paper Citation Record · LEDGER

An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2205.06308.

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

pith.paper-citation-record.v1
2205.06308 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:51.243958Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:40:24.715840Z

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 2250c840-d260-4a64-a1a1-8565d18c2ac1 · inbound

Models of Heavy-Tailed Mechanistic Universality cites this paper.

Models of Heavy-Tailed Mechanistic Universality An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:51.243958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 008382cc-922d-414d-b648-0d355eddfe4f · inbound

Statistical Limits for Finite-Rank Tensor Estimation cites this paper.

Statistical Limits for Finite-Rank Tensor Estimation An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T05:54:49.309883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24b7dcc2-93f8-4564-bac0-c7baa3bd21b6 · inbound

On the edge eigenvalues of sparse random geometric graphs cites this paper.

On the edge eigenvalues of sparse random geometric graphs An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T22:29:25.849236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23bebdb2-70af-4328-aed8-3cb5966dc52d · inbound

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model cites this paper.

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:39:38.357722Z

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 08807bbf-eed0-48a8-8aff-3dfa07917781 · 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 An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:32:56.128469Z

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 4565ba64-099e-44b2-b523-8363dee5dd5f · 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 An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings

Reference 166

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:40:24.719338Z

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.