Paper Citation Record · LEDGER

Learning New Physics from an Imperfect Machine

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2111.13633.

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

pith.paper-citation-record.v1
2111.13633 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:19:24.157791Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:58:54.757253Z

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 f0eea13d-c2d6-4af2-93aa-89d3e0b74b99 · inbound

Enhancing anomaly detection with topology-aware autoencoders cites this paper.

Enhancing anomaly detection with topology-aware autoencoders Learning New Physics from an Imperfect Machine

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T19:19:24.157791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53ea46ea-0ca6-4ba3-bb3d-4221c6e6e808 · inbound

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties cites this paper.

Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties Learning New Physics from an Imperfect Machine

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:03.806105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80fd4895-63b5-488b-bff3-05a36bf70296 · inbound

Local Conformal Predictions for Calibrated Surrogates cites this paper.

Local Conformal Predictions for Calibrated Surrogates Learning New Physics from an Imperfect Machine

Reference 200

Resolution: verified exact; arxiv_id, observed 2026-07-03T19:58:54.758487Z

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.

Observation cdcf3cb5-f322-4676-969d-fdbafce0a981 · inbound

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough cites this paper.

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough Learning New Physics from an Imperfect Machine

Reference 129

Resolution: unresolved; no resolver link, observed 2026-07-14T00:50:24.285797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.