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Paper Citation Record · LEDGER

Generative models on phase space

As of 6 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 1 inbound Pith citation observation for arXiv:2604.02415.

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

pith.paper-citation-record.v1
2604.02415 v2

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-13T20:52:29.032797Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T21:19:37.930502Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

77 of 77 outbound references displayed

  • verified exact59
  • verified fuzzy10
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation af560131-7889-4b97-8eab-997eecbbe44c · outbound

This paper cites an unresolved cited work.

Generative models on phase space Unresolved cited work

Reference 1

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Observation 1c60d74e-60bb-4dea-96f1-078c24dc5c3e · outbound

This paper cites copies” the phase space submanifoldN mult times intoq-space; 7 •using adifferent(b, x) for each pointPto continously “fill out.

Generative models on phase space copies” the phase space submanifoldN mult times intoq-space; 7 •using adifferent(b, x) for each pointPto continously “fill out

Reference 2

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Observation 4d893860-74b3-454d-a62d-0978171e7701 · outbound

This paper cites Diffusion time is encoded through sinusoidal embeddings with dimension 64, appended to the 9-dimensional input for 3-particleq-space.

Generative models on phase space Diffusion time is encoded through sinusoidal embeddings with dimension 64, appended to the 9-dimensional input for 3-particleq-space

Reference 3

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Observation 8d31853a-01ac-4f6c-9103-dff46cdeea60 · outbound

This paper cites interpretingp-space vectors directly asq-space vectors).

Generative models on phase space interpretingp-space vectors directly asq-space vectors)

Reference 4

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Observation f23f6bbc-7fbc-48cb-ac2d-eb301ed5d56d · outbound

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Generative models on phase space Unresolved cited work

Reference 5

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Observation 74759154-d09b-49b5-85f3-b2f815af1096 · outbound

This paper cites 16: Training data inq-space for the different data augmentation strategies in Fig.

Generative models on phase space 16: Training data inq-space for the different data augmentation strategies in Fig

Reference 6

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Observation fb71662e-5bec-4b92-98a4-f996a689cda9 · outbound

This paper cites FID inv” denotes a Fr´ echet distance computed on Lorentz-invariant features, while “FIDAE.

Generative models on phase space FID inv” denotes a Fr´ echet distance computed on Lorentz-invariant features, while “FIDAE

Reference 7

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Observation 9576933c-5282-4642-aa3f-f4843a6548db · outbound

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Generative models on phase space Unresolved cited work

Reference 8

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Observation 6cd763c7-d604-4cf9-94cf-dd8a6f26a48a · outbound

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Generative models on phase space Unresolved cited work

Reference 9

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Observation 3702b059-f05b-4ea6-85ed-0aabc6ca460e · outbound

This paper cites Testing the Manifold Hypothesis.

Generative models on phase space Testing the Manifold Hypothesis

Reference 10

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Observation 521f8746-9efd-4044-8bce-8373dab1d138 · outbound

This paper cites Representation Learning: A Review and New Perspectives.

Generative models on phase space Representation Learning: A Review and New Perspectives

Reference 11

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Observation 28f07478-0585-4e9c-aa91-f5e92394cd1e · outbound

This paper cites an unresolved cited work.

Generative models on phase space Unresolved cited work

Reference 12

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Observation d00e828b-da49-47e7-a6e7-bed16014e698 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Generative models on phase space Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 13

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Observation 6b2b3d9a-79a5-4214-965b-59d315119e8d · outbound

This paper cites Generative Modeling by Estimating Gradients of the Data Distribution.

Generative models on phase space Generative Modeling by Estimating Gradients of the Data Distribution

Reference 14

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Observation 3cab11af-9bd2-4cde-891f-62ecdfe11f57 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Generative models on phase space Denoising Diffusion Probabilistic Models

Reference 15

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Observation 46d954a1-acb4-4d00-8117-01f1f77fb7da · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generative models on phase space Score-Based Generative Modeling through Stochastic Differential Equations

Reference 16

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Observation 5f9fcd0c-d03d-493f-b833-c3ce8547791a · outbound

This paper cites Flow Matching for Generative Modeling.

Generative models on phase space Flow Matching for Generative Modeling

Reference 17

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Observation 4d7d631f-40fb-4e01-a1d6-3098f4faf97b · outbound

This paper cites Flow Matching Guide and Code.

Generative models on phase space Flow Matching Guide and Code

Reference 18

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Observation acf2a570-9d7a-4a86-a660-12c3c6344142 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Generative models on phase space Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 19

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Observation 4eb40281-e09e-4699-b863-ef8fe57013f9 · outbound

This paper cites The Principles of Diffusion Models.

Generative models on phase space The Principles of Diffusion Models

Reference 20

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Observation cde39713-8d20-4c06-9b33-045b70385845 · outbound

This paper cites PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics.

Generative models on phase space PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics

Reference 21

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Observation e08f2758-cac9-4393-9c75-2e4a20c98510 · outbound

This paper cites Fast Point Cloud Generation with Diffusion Models in High Energy Physics.

Generative models on phase space Fast Point Cloud Generation with Diffusion Models in High Energy Physics

Reference 22

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Observation fb11b7ec-6d49-4ff2-aa25-4443aa19095d · outbound

This paper cites Jet Diffusion versus JetGPT -- Modern Networks for the LHC.

Generative models on phase space Jet Diffusion versus JetGPT -- Modern Networks for the LHC

Reference 23

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Observation bcba2ce3-eb77-41d6-893c-db41bb63fe66 · outbound

This paper cites PC-Droid: Faster diffusion and improved quality for particle cloud generation.

Generative models on phase space PC-Droid: Faster diffusion and improved quality for particle cloud generation

Reference 24

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arxiv_id, observed 2026-05-13T20:53:15.372965Z

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Observation 376c4e9c-bbb5-46e6-a861-a0b5d54cdccb · outbound

This paper cites EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion.

Generative models on phase space EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion

Reference 25

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arxiv_id, observed 2026-05-13T20:53:15.457370Z

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Observation 2cef89b0-a722-4128-abcf-9e7b74ef619f · outbound

This paper cites Kicking it Off(-shell) with Direct Diffusion.

Generative models on phase space Kicking it Off(-shell) with Direct Diffusion

Reference 26

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Observation b185a131-7c6d-4e5f-8a17-c3077bdc28d9 · outbound

This paper cites Improving new physics searches with diffusion models for event observables and jet constituents.

Generative models on phase space Improving new physics searches with diffusion models for event observables and jet constituents

Reference 27

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Observation 0e3b190c-1963-49c7-a636-33dab26b8862 · outbound

This paper cites PIPPIN: Generating variable length full events from partons.

Generative models on phase space PIPPIN: Generating variable length full events from partons

Reference 28

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Observation d33f6082-3761-453b-9142-0f3cee2b73f0 · outbound

This paper cites Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation.

Generative models on phase space Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation

Reference 29

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Observation b40888e1-cbc8-40cf-bf9e-1605844d7a38 · outbound

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Generative models on phase space Mikuni and B

Reference 30

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Observation 6d6a0b61-63a6-4571-a984-0be77fba94af · outbound

This paper cites Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events.

Generative models on phase space Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events

Reference 31

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arxiv_id, observed 2026-05-13T20:53:15.473595Z

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Observation 6d99f82c-747f-4af6-af2b-ea6e2c63e5fc · outbound

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Generative models on phase space Unresolved cited work

Reference 32

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Observation 3b2a67be-b1d4-4965-9927-ecd68fb8502c · outbound

This paper cites Bhimji, C.

Generative models on phase space Bhimji, C

Reference 33

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Observation 1519133a-4d00-4356-bfd2-a402c4c3565d · outbound

This paper cites An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging.

Generative models on phase space An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging

Reference 34

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arxiv_id, observed 2026-05-13T20:53:15.470980Z

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Observation 537e1b3b-9387-4e66-a6d0-b58596527b8a · outbound

This paper cites PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics.

Generative models on phase space PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics

Reference 35

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Observation 48bf5b58-fb7e-4f85-927f-a386d79994af · outbound

This paper cites Lorentz group equivariant autoencoders.

Generative models on phase space Lorentz group equivariant autoencoders

Reference 36

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arxiv_id, observed 2026-05-13T20:53:15.468248Z

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Observation 22b05353-81ce-42af-9f08-57e23171f64f · outbound

This paper cites Explainable Equivariant Neural Networks for Particle Physics: PELICAN.

Generative models on phase space Explainable Equivariant Neural Networks for Particle Physics: PELICAN

Reference 37

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Observation f04d37b0-c03b-4426-ac20-33cc8d3ea759 · outbound

This paper cites Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics.

Generative models on phase space Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics

Reference 38

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Observation d18010e1-7a5e-4ea2-8d5d-a2c2a6267740 · outbound

This paper cites Spinner, L.

Generative models on phase space Spinner, L

Reference 39

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Observation 7cb51ebc-d445-4a30-b65d-b77709c8bc26 · outbound

This paper cites Hebbar, T.

Generative models on phase space Hebbar, T

Reference 40

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Observation 7ffb39a3-0788-4f6f-a990-61dea6c88d6a · outbound

This paper cites Poisson Flow Generative Models.

Generative models on phase space Poisson Flow Generative Models

Reference 41

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Observation d8044aa8-94bd-48e9-b2e5-e408c21439c5 · outbound

This paper cites PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.

Generative models on phase space PFGM++: Unlocking the Potential of Physics-Inspired Generative Models

Reference 42

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Observation a9a36e32-eacf-481f-bb80-e210c65d385c · outbound

This paper cites Riemannian Score-Based Generative Modelling.

Generative models on phase space Riemannian Score-Based Generative Modelling

Reference 43

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Observation 23204d30-1252-4330-89a6-aa180e5e17fc · outbound

This paper cites Diffusion Processes on Implicit Manifolds.

Generative models on phase space Diffusion Processes on Implicit Manifolds

Reference 44

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local_arxiv, observed 2026-05-13T20:53:15.510130Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation e90aed64-bbbe-49f9-bdab-b74631acbd5d · outbound

This paper cites Kleiss, W.

Generative models on phase space Kleiss, W

Reference 45

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 4dbb3ef3-4d00-4330-91ec-3efe44763ae2 · outbound

This paper cites ELSA -- Enhanced latent spaces for improved collider simulations.

Generative models on phase space ELSA -- Enhanced latent spaces for improved collider simulations

Reference 46

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arxiv_id, observed 2026-05-13T20:53:15.420849Z

Source-reported events for the cited work

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Observation a717bfa1-8f5f-4e62-99b9-0e6d7c0758bb · outbound

This paper cites Differentiable MadNIS-Lite.

Generative models on phase space Differentiable MadNIS-Lite

Reference 47

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verified exact
arxiv_id, observed 2026-05-13T20:53:15.488959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation b0fccbd3-4c9a-4d49-824b-f0a8ea288cc8 · outbound

This paper cites The Hidden Geometry of Particle Collisions.

Generative models on phase space The Hidden Geometry of Particle Collisions

Reference 48

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 23058e8f-32fd-4128-86c8-aa467df63591 · outbound

This paper cites Mirrored Langevin Dynamics.

Generative models on phase space Mirrored Langevin Dynamics

Reference 49

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verified exact
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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 058c8c9c-2100-4dbe-88c4-ec2d7b290b48 · outbound

This paper cites Hyv¨ arinen and P.

Generative models on phase space Hyv¨ arinen and P

Reference 50

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 17e7c856-3df8-4f98-94ad-9a4ea4f349f6 · outbound

This paper cites A comprehensive survey on data augmentation.

Generative models on phase space A comprehensive survey on data augmentation

Reference 51

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 12c4b507-421b-4f4f-bed9-29d4c0dfae3c · outbound

This paper cites Topological Obstructions to Autoencoding.

Generative models on phase space Topological Obstructions to Autoencoding

Reference 52

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation e7f1b79c-4ea2-46ab-9b82-614b7008f90e · outbound

This paper cites Rosenblatt, Remarks on a Multivariate Transformation, Annals of Mathematical Statistics23, 470 (1952).

Generative models on phase space Rosenblatt, Remarks on a Multivariate Transformation, Annals of Mathematical Statistics23, 470 (1952)

Reference 53

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Observation 3f19c19f-cb0d-4146-aa05-76a0fe69a767 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Generative models on phase space The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 54

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation cc6d062a-413d-486c-9115-b07e51789a10 · outbound

This paper cites Brandt, C.

Generative models on phase space Brandt, C

Reference 55

Resolution
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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 49ff0078-79d3-4c7a-be7c-3ccc8e161bd6 · outbound

This paper cites Farhi, A QCD Test for Jets, Phys.

Generative models on phase space Farhi, A QCD Test for Jets, Phys

Reference 56

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verified fuzzy
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Observation 3d42721a-ba82-4cc6-b269-f91247b6fcc5 · outbound

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Generative models on phase space Unresolved cited work

Reference 57

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Observation 8c653e77-3df9-446c-be42-443b7176ba8f · outbound

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Generative models on phase space Unresolved cited work

Reference 58

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 747e91b5-c2d4-4eff-95c7-44ecfdc59c78 · outbound

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Generative models on phase space Unresolved cited work

Reference 59

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 1921619d-b332-4ffe-a29a-66fff71cedde · outbound

This paper cites SARGE: an algorithm for generating QCD-antennas.

Generative models on phase space SARGE: an algorithm for generating QCD-antennas

Reference 60

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 264f4e21-4942-40a8-a78b-5ca951335c2d · outbound

This paper cites A Theory of Quark vs. Gluon Discrimination.

Generative models on phase space A Theory of Quark vs. Gluon Discrimination

Reference 61

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 1f906dc1-43ca-48d0-b966-331755505f19 · outbound

This paper cites Explainable Machine Learning for Scientific Insights and Discoveries.

Generative models on phase space Explainable Machine Learning for Scientific Insights and Discoveries

Reference 62

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arxiv_id, observed 2026-05-13T20:53:15.387633Z

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Observation e703f4fd-c686-46a2-87ae-ab89e98847aa · outbound

This paper cites On scientific understanding with artificial intelligence.

Generative models on phase space On scientific understanding with artificial intelligence

Reference 63

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation c5948c6d-e2c5-46a6-af96-fa1b97516b31 · outbound

This paper cites Gambhir, M.

Generative models on phase space Gambhir, M

Reference 64

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 5bf6fd5a-d3d4-4285-86ca-ca7442b63242 · outbound

This paper cites How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model.

Generative models on phase space How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Reference 65

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 72a8dc45-1d4f-4bf0-b378-f75b0059128b · outbound

This paper cites A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data.

Generative models on phase space A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data

Reference 66

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation c398a5e7-f691-42da-8092-6ed553c4e734 · outbound

This paper cites Probing the Latent Hierarchical Structure of Data via Diffusion Models.

Generative models on phase space Probing the Latent Hierarchical Structure of Data via Diffusion Models

Reference 67

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 6bd24b0e-733b-4afe-8ae4-bec42b30de0e · outbound

This paper cites How Compositional Generalization and Creativity Improve as Diffusion Models are Trained.

Generative models on phase space How Compositional Generalization and Creativity Improve as Diffusion Models are Trained

Reference 68

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 0d9027ab-1f78-4483-8071-9aa8c33bbeb6 · outbound

This paper cites Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?.

Generative models on phase space Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?

Reference 69

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verified exact
arxiv_id, observed 2026-05-13T20:53:15.426862Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 3b54f798-f71c-47eb-8fee-a776a6b99fcb · outbound

This paper cites The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets.

Generative models on phase space The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets

Reference 70

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation d33ef6dc-53ec-4b64-a195-45be8b1b2bc3 · outbound

This paper cites The Universal Statistical Structure and Scaling Laws of Chaos and Turbulence.

Generative models on phase space The Universal Statistical Structure and Scaling Laws of Chaos and Turbulence

Reference 71

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arxiv_id, observed 2026-05-13T20:53:15.481323Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation ca70012f-cba8-40a4-9c69-456078a47767 · outbound

This paper cites Exploring the Space of Jets with CMS Open Data.

Generative models on phase space Exploring the Space of Jets with CMS Open Data

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:53:15.404547Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 5ec1945c-1260-463f-96ad-d547622d7fe3 · outbound

This paper cites Explicit or Implicit? Encoding Physics at the Precision Frontier.

Generative models on phase space Explicit or Implicit? Encoding Physics at the Precision Frontier

Reference 73

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arxiv_id, observed 2026-08-03T03:15:37.812029Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation c9035587-1a3f-4f17-8308-97f2b1544ed4 · outbound

This paper cites Omnicos- mos: Transferring particle physics knowledge across the cosmos.

Generative models on phase space Omnicos- mos: Transferring particle physics knowledge across the cosmos

Reference 74

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verified exact
arxiv_id, observed 2026-05-13T20:53:15.494208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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This paper cites OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers.

Generative models on phase space OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers

Reference 75

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Observation 274c7c9a-e6dd-4cf2-8d35-1b74659de8aa · outbound

This paper cites Vincent, A connection between score matching and denoising autoencoders, Neural computation23, 1661 (2011).

Generative models on phase space Vincent, A connection between score matching and denoising autoencoders, Neural computation23, 1661 (2011)

Reference 76

Resolution
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raw_fallback, observed 2026-05-14T00:53:35.525172Z

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Observation c3903008-8925-4dbb-8067-6c56ce932080 · outbound

This paper cites Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates.

Generative models on phase space Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates

Reference 77

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:53:15.515801Z

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Pith citing papers

Observation 606df074-c5b0-46c1-b99c-1abc4c488a93 · 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 Generative models on phase space

Reference 29

Resolution
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