Paper Citation Record · LEDGER

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2205.01445.

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

pith.paper-citation-record.v1
2205.01445 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:32:00.230044Z

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

11
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 a7f9ffab-57fd-42bd-9aff-088f1a92a856 · inbound

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models cites this paper.

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:00.230044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abf84bb4-9c10-4673-b935-749f972b6cb5 · inbound

Sharp convergence rates for Spectral methods via the feature space decomposition method cites this paper.

Sharp convergence rates for Spectral methods via the feature space decomposition method High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:34:17.291745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cb80a9ad-fa46-435a-b81f-c1b52a47a47c · inbound

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:31:22.885759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72d5132f-f84e-42f0-aae8-2eacff7a69b1 · inbound

Spectral phase transitions and trainability in neural network learning dynamics cites this paper.

Spectral phase transitions and trainability in neural network learning dynamics High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:35:47.479702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.