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Paper Citation Record · LEDGER

Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2009.06754.

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

pith.paper-citation-record.v1
2009.06754 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:30:19.253360Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T17:14:59.596593Z

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 cbdd98d5-e7ed-45af-b120-d2170af2b54f · inbound

Transformer networks for Heavy flavor jet tagging cites this paper.

Transformer networks for Heavy flavor jet tagging Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T18:30:19.253360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:30:19.253360Z digest=sha256:c14dc7e38fe1211aeab113cf68786b124744ef46e9871830910dd90ce3dc2348

Observation 0ea84e1f-bd63-47a8-83e1-728abd92d6ef · inbound

IAFormer: Interaction-Aware Transformer network for collider data analysis cites this paper.

IAFormer: Interaction-Aware Transformer network for collider data analysis Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-22T17:14:59.598280Z

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=2026-05-22T17:13:47.293753Z digest=sha256:cd3e2be5c727f060422e8f2dc83ae84b250e82e34cbb8c62ed11432e9836760b