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Paper Citation Record · LEDGER

Black holes and the loss landscape in machine learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.14817.

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

pith.paper-citation-record.v1
2306.14817 v1

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:05:16.373378Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 73ff5b57-a232-49fd-b6cb-5121752a37da · inbound

Pure D-brane Black Holes: BPS Counting and non-BPS Vacua cites this paper.

Pure D-brane Black Holes: BPS Counting and non-BPS Vacua Black holes and the loss landscape in machine learning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-03T11:05:16.373378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:05:16.373378Z digest=sha256:1731a896c0200eeb8b1f1486f45c937c9028a3a6b8b71930ebf2317f51dfd78b

Observation c2a9b78a-1914-49c9-b714-51b9064a8558 · inbound

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks cites this paper.

Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks Black holes and the loss landscape in machine learning

Reference 43

Resolution
unresolved
no resolver link, observed 2026-07-11T06:56:17.499687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T06:56:17.499687Z digest=sha256:377a474342d019f99c4f50f7cd62cf9014a8874d07ed30339d0d5333c85cfec0