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Paper Citation Record · LEDGER

Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2109.11964.

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

pith.paper-citation-record.v1
2109.11964 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:42:04.646977Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T08:09:51.510835Z

Reference resolution

0 of 0 outbound references displayed

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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 398b4ebe-bf13-48bb-80f3-d58aaa7cf2e0 · inbound

LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning cites this paper.

LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T12:42:04.646977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 72d5c772-2c25-49fb-800e-41b34cb25710 · inbound

How to Unfold Top Decays cites this paper.

How to Unfold Top Decays Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-10T17:18:46.072316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:18:46.072316Z digest=sha256:e0d687716c144aa56e4baf0f5f8b57f563c204e8262b6ea7da50b89f38af5c03

Observation 07eca47b-c78f-474b-94a1-855d6cadfdac · inbound

Amplitude Uncertainties Everywhere All at Once cites this paper.

Amplitude Uncertainties Everywhere All at Once Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:21:47.099032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-18T19:21:24.292766Z digest=sha256:a0d57cbc7fc9ac3333c2ef4ca9006a2ebcbcae852d9194ce5574a728093a4336

Observation 51425970-6a9a-4551-bcd5-7a63cd551b4b · inbound

FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD cites this paper.

FASTColor -- Full-color Amplitude Surrogate Toolkit for QCD Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-04T22:58:02.166540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:58:02.166540Z digest=sha256:82c82a2b8aa9495a77e215640f9fd896660efc8c96bae619a36fdf3c9dd14a22

Observation 929de65a-0e7a-47a1-8278-511411a51c79 · inbound

A Novel Implementation of the Matrix Element Method at Next-to-Leading Order for the Measurement of the Higgs Self-Coupling ${\lambda}_{3H}$ cites this paper.

A Novel Implementation of the Matrix Element Method at Next-to-Leading Order for the Measurement of the Higgs Self-Coupling ${\lambda}_{3H}$ Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-03T05:27:31.600692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T05:27:31.600692Z digest=sha256:d1c27c7c19706c7268de9b8966cb8e9b33d8d3b9f43070e5ffc6857e6f8969df

Observation d724ce0d-ba19-4adc-8963-5f29b23164ec · inbound

Resonance-aware parton-shower matching for off-shell top-antitop production with semi-leptonic decays at electron-positron colliders cites this paper.

Resonance-aware parton-shower matching for off-shell top-antitop production with semi-leptonic decays at electron-positron colliders Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-02T20:58:39.563554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:58:39.563554Z digest=sha256:81ac430cbee83b920883de10028619ca9768c275cac48758d171f54e9371b36c

Observation 581c801f-3729-4089-918a-7bf02c2e099a · inbound

Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms cites this paper.

Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-20T09:43:10.746273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-20T09:42:31.642024Z digest=sha256:5ec929ba6a7703da4c718db2773ce583f80ef8578bc41082b7b9e7f587a355d8

Observation 427780fe-90b7-4b88-a107-c6994dee91bb · inbound

Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms cites this paper.

Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-21T08:09:51.512337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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