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Paper Citation Record · LEDGER

A Novel Gaussian Min-Max Theorem and its Applications

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.07356.

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

pith.paper-citation-record.v1
2402.07356 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:37:13.234393Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T18:33:06.902020Z

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 7197a8f6-aa46-454f-b641-3c5e4eec63f7 · inbound

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation cites this paper.

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation A Novel Gaussian Min-Max Theorem and its Applications

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-08T14:37:13.234393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T14:37:13.234393Z digest=sha256:2ee1068485e4b6784794288475bdadfdc222382a0cb842a8e2f8ada851e5548a

Observation b8d7337c-2da4-4894-8f2f-c2a844b33db6 · inbound

Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization cites this paper.

Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization A Novel Gaussian Min-Max Theorem and its Applications

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-13T18:33:06.903934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-13T18:28:32.915468Z digest=sha256:80f4b07f567c4ddd8cc0e226ccb05d6944f6f48ca2144a760f958eea45d7a50b