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Paper Citation Record · LEDGER

AI Safety for High Energy Physics

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.08606.

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

pith.paper-citation-record.v1
1910.08606 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:51:03.171517Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T09:49:18.062053Z

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 9b5c03bf-d9b5-49d5-807b-a9ee81516e00 · inbound

Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface cites this paper.

Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface AI Safety for High Energy Physics

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-24T09:49:18.065447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-24T09:47:11.947061Z digest=sha256:d545d2319a45ccf0c464848e0ff0034b4de8da5c3f6fe8f3e206dfa35b61a640

Observation d5099190-c0c3-4267-84bb-bdb5c8214db1 · inbound

Contrastive Learning for Robust Representations of Neutrino Data cites this paper.

Contrastive Learning for Robust Representations of Neutrino Data AI Safety for High Energy Physics

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-08T11:51:03.171517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:51:03.171517Z digest=sha256:3980b0c6dd5c0ce74984b072362c82365510895fa85355951b758b45da6ef264