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Paper Citation Record · LEDGER

A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2208.03313.

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

pith.paper-citation-record.v1
2208.03313 v2

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 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:12:14.416167Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:01:00.953972Z

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 1dc2251c-7e3d-401f-84c7-df86569ef0cc · inbound

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators cites this paper.

Multi-Environment GLAMP: Approximate Message Passing for Transfer Learning with Applications to Lasso-based Estimators A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T13:12:14.416167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:12:14.416167Z digest=sha256:e08d79ff401995555cf844d554bd2be65ef50dfb92f2ee3eff877dc4ef33cb66

Observation c9a2570a-79ed-4829-9d48-5785e81433df · inbound

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime cites this paper.

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-04T13:54:25.534737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T13:54:25.534737Z digest=sha256:5c332eac199efa3e0ee4ad75a527501291db4edacee67211daa2897ddbd71121

Observation 3c14d90a-601e-4c74-b414-a08b093b0d6c · inbound

Markov Chains Approximate Message Passing cites this paper.

Markov Chains Approximate Message Passing A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-03T19:13:11.507141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:13:11.507141Z digest=sha256:7d1586af66ba996c6ce037e098f3ca549772f2fbdf02c0d471ee0c3ccf248eb8

Observation 998464e5-80e7-49db-8d3d-0e7d53c15578 · inbound

Universality of first-order methods on random and deterministic matrices cites this paper.

Universality of first-order methods on random and deterministic matrices A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:01:00.964338Z

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.

source=pdf_text observed=2026-05-10T16:17:55.556535Z digest=sha256:1e3d274c3dcb1af99e8dbb3aa86ac98cfc9e5518a428a33ac6ae3a3e4aacb491

Observation 59f4a840-5719-4d51-b09a-5937cb23f3b4 · inbound

Approximate Message Passing with Random Initialization for Phase Retrieval cites this paper.

Approximate Message Passing with Random Initialization for Phase Retrieval A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models

Reference 136

Resolution
unresolved
no resolver link, observed 2026-08-04T23:35:25.861764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T23:35:25.861764Z digest=sha256:0dfc533445899414d86cfd8625a8d501ac59c0ea42f77a103bbaf1ba64455216