Paper Citation Record · LEDGER

Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2308.03876.

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

pith.paper-citation-record.v1
2308.03876 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:27:36.602808Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T08:57:48.000195Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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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 949c8279-25ff-408e-a627-3382bc521b9a · inbound

GPT-like transformer model for silicon tracking detector simulation cites this paper.

GPT-like transformer model for silicon tracking detector simulation Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T13:27:36.602808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e4788e-c104-4edd-b926-a41e3f4c7933 · inbound

BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation cites this paper.

BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 19

Resolution: malformed identifier; arxiv_id, observed 2026-05-11T19:16:09.231234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca790790-91c7-41e3-8b77-1c06cbcb916d · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:06:41.294695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e84fc515-135c-4c04-8909-a97cb1d39ebc · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T12:30:40.143174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e063bda2-3fff-4a82-b28f-643539137adb · inbound

SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation cites this paper.

SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:57:48.001502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7ff50389-69f9-4028-8a11-28d59b4fb38d · inbound

Learning Standard Model structure from LHC data with Riemannian flow matching cites this paper.

Learning Standard Model structure from LHC data with Riemannian flow matching Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:36.969355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.