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Paper Citation Record · LEDGER

Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.18274.

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

pith.paper-citation-record.v1
2405.18274 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:40.662641Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:40:24.821249Z

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 49b4fddf-ba51-4679-b613-98ca54407e0b · inbound

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning cites this paper.

On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-09T14:47:40.662641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:47:40.662641Z digest=sha256:d4e5ba1be7ee340fe72af566c3f8d49302297ce3a7a3f85ffb6d2f1d265e7e34

Observation 8db58a40-72b7-4e2f-8141-f6b513972f9a · inbound

Fluctuations of the largest eigenvalues of transformed spiked Wigner matrices cites this paper.

Fluctuations of the largest eigenvalues of transformed spiked Wigner matrices Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-08T21:53:56.125928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T21:53:56.125928Z digest=sha256:a1fd55d67c6523f8bb7e79c5ed006cdae740d07c627f0165b4347393e4a135b3

Observation 10681d7c-7428-487c-8ee3-b45b04344f85 · inbound

On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective cites this paper.

On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T14:40:55.339442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:40:55.339442Z digest=sha256:0d83924828116b93222e2f082c0141fb1917ed901aae65d7d3addccaafb13056

Observation 5c89c5ae-b727-4f51-8ca3-772f670263f8 · 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 Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Reference 294

Resolution
verified exact
arxiv_id, observed 2026-05-20T01:32:56.017185Z

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.

source=arxiv_source observed=2026-05-20T01:29:14.555216Z digest=sha256:bd8ba5b913475e4c30b6bce3eae9eeba4b9121e7843eef6a81c9689555c445ba

Observation be0c384d-59d3-489c-8ace-ac50c5e3aa5c · 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 Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models

Reference 294

Resolution
verified exact
arxiv_id, observed 2026-05-25T06:40:24.824537Z

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.

source=arxiv_source observed=2026-05-25T06:39:16.246591Z digest=sha256:d19e146aade570d9732f2023732f37b46dfe233efc0ff878c32de750fcf96168