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Paper Citation Record · LEDGER

Toward an AI Physicist for Unsupervised Learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.10525.

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

pith.paper-citation-record.v1
1810.10525 v4

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 2026-08-11T17:53:24.444670Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T09:21:20.987101Z

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 0216446b-57fb-4020-a45e-17d3160b3153 · inbound

Discover physical concepts and equations with machine learning cites this paper.

Discover physical concepts and equations with machine learning Toward an AI Physicist for Unsupervised Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T17:53:24.444670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:53:24.444670Z digest=sha256:4cdfb6c10467588b280ba0537a849dde5bbabddeaf5ff3d42fd7c71ad11634a2

Observation b58275cc-e2fe-459f-b4d2-4a0a9e74e3c0 · inbound

Predicting intermediate-mass black hole formation in star clusters with machine learning cites this paper.

Predicting intermediate-mass black hole formation in star clusters with machine learning Toward an AI Physicist for Unsupervised Learning

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-22T09:21:20.989539Z

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=pdf_text observed=2026-05-22T09:20:09.976842Z digest=sha256:bb8072751a5d8e2e55c8a1a4fc7b904896194e57681658ba796944032a48b8f0