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Paper Citation Record · LEDGER

Three-Body Scattering for Generative Modeling

As of 13 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 1 inbound Pith citation observation for arXiv:2607.18198.

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

pith.paper-citation-record.v1
2607.18198 v1

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:49:34.760798Z

measured 77 of 77 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-30T18:58:28.532532Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

76 of 76 outbound references displayed

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  • verified fuzzy0
  • unresolved76
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ccb96af3-00c8-4a2a-b164-b8e8aaa601c0 · outbound

This paper cites Gradient Flows in Metric Spaces and in the Space of Probability Measures.

Three-Body Scattering for Generative Modeling Gradient Flows in Metric Spaces and in the Space of Probability Measures

Reference 1

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source=arxiv_source observed=2026-08-01T15:49:29.959633Z digest=sha256:6b7b04ae0c0ffd5211a78c89d6bdb39684840c1d2d175dabf84c211b6b7d82f0

Observation 22f355e6-763a-474f-9a02-9f20f91cee05 · outbound

This paper cites Maximum mean discrepancy gradient flow.

Three-Body Scattering for Generative Modeling Maximum mean discrepancy gradient flow

Reference 2

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source=arxiv_source observed=2026-08-01T15:49:30.000157Z digest=sha256:30c569bf3a0e7a3833d03729bfb084f2469ca2071b42b37077f34485412d2c8c

Observation ff227714-887f-4bba-b521-f00bb1013b6b · outbound

This paper cites Qwen2.5-VL Technical Report.

Three-Body Scattering for Generative Modeling Qwen2.5-VL Technical Report

Reference 3

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source=arxiv_source observed=2026-08-01T15:49:30.078314Z digest=sha256:c9db212c23cc2439468abb2f4ea2283e024971b89e127d3c1a60c5d96199c495

Observation 6e8dcb30-8f4d-423c-ab3a-d7acf937380d · outbound

This paper cites The Cramer Distance as a Solution to Biased Wasserstein Gradients.

Three-Body Scattering for Generative Modeling The Cramer Distance as a Solution to Biased Wasserstein Gradients

Reference 4

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source=arxiv_source observed=2026-08-01T15:49:30.146804Z digest=sha256:3540d4477c2d02ffe77b825d5c36b224e32511f15e6c907e000ab0675096df99

Observation 90e89fb6-bbcb-4a55-83b1-aafb06aa62b1 · outbound

This paper cites On the global convergence of Wasserstein gradient flow of the Coulomb discrepancy.

Three-Body Scattering for Generative Modeling On the global convergence of Wasserstein gradient flow of the Coulomb discrepancy

Reference 5

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source=arxiv_source observed=2026-08-01T15:49:30.198557Z digest=sha256:af91ec76ecb8c11766c8b2b448f56fd5fdb25ddcc4ac147d3033fea2e614b6cc

Observation ef6926c7-d441-45a4-bdd9-0fdd49f567a0 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Three-Body Scattering for Generative Modeling Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 6

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source=arxiv_source observed=2026-08-01T15:49:30.266156Z digest=sha256:a19374c9afbfabee8206204579aae4d0d6d2fde64ef3b84a7d6fafbf8b0d4cb9

Observation c25037bd-be54-41a3-87da-5949586d31bc · outbound

This paper cites A Unifying View of Variational Generative Wasserstein Flows.

Three-Body Scattering for Generative Modeling A Unifying View of Variational Generative Wasserstein Flows

Reference 7

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source=arxiv_source observed=2026-08-01T15:49:30.314806Z digest=sha256:8b9d30fd4367997f06ffc230642b4e73640d2f41f7c98244e8aad6a911d5016d

Observation 3bb6ca6c-dfa5-437b-b366-99a37d75874d · outbound

This paper cites Quantitative convergence of Wasserstein gradient flows of kernel mean discrepancies.

Three-Body Scattering for Generative Modeling Quantitative convergence of Wasserstein gradient flows of kernel mean discrepancies

Reference 8

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source=arxiv_source observed=2026-08-01T15:49:30.356867Z digest=sha256:3a9b2bf0c44365a214f81c04f7a76a0a3fd79b1598c1f5e38d5d8f691564f0aa

Observation f9ff2f15-aa23-4b9b-b40c-161b3367047c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Three-Body Scattering for Generative Modeling Imagenet: A large-scale hierarchical image database

Reference 9

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source=arxiv_source observed=2026-08-01T15:49:30.428288Z digest=sha256:730a29253c276edee5c4fc9a1b97b4e00a9ee3b22969d5e48054696f3f3bfa55

Observation 7447b823-0901-48a3-9f58-b6eea469ebcf · outbound

This paper cites Generative Modeling via Drifting.

Three-Body Scattering for Generative Modeling Generative Modeling via Drifting

Reference 10

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source=arxiv_source observed=2026-08-01T15:49:30.489900Z digest=sha256:dd801777ee2f6d0f2af8bf234192b3ab8f575eb0b2145995b1071bfffd4a0d9f

Observation c03e923c-2ef3-44d0-9c6e-7c8c36f7094b · outbound

This paper cites Diffusion models beat gans on image synthesis.

Three-Body Scattering for Generative Modeling Diffusion models beat gans on image synthesis

Reference 11

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source=arxiv_source observed=2026-08-01T15:49:30.565816Z digest=sha256:908b4a729cfc43e37dab3410b6f8cc9cf6befa091dc334db5fd8521446a37a7d

Observation d1170246-7938-4416-b30b-32854e9473be · outbound

This paper cites Wasserstein gradient flows of MMD functionals with distance kernel and cauchy problems on quantile functions.

Three-Body Scattering for Generative Modeling Wasserstein gradient flows of MMD functionals with distance kernel and cauchy problems on quantile functions

Reference 12

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source=arxiv_source observed=2026-08-01T15:49:30.643522Z digest=sha256:de3bdbc8ee7e6f9758f973b4ca3174848d8f02a78bbaaa07b60cd5b7c31ea84c

Observation 03a2fb26-3aa5-469c-9a80-25d9b844acbc · outbound

This paper cites Kernel-Gradient Drifting Models.

Three-Body Scattering for Generative Modeling Kernel-Gradient Drifting Models

Reference 13

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source=arxiv_source observed=2026-08-01T15:49:30.711627Z digest=sha256:3b34ec27a4e1e687744fde9eaf9acc410c36d1ddfe56134c2953a3c138879fd1

Observation e7399d7a-98d4-4d8a-a1e0-c778cb66d3e7 · outbound

This paper cites Representation Distribution Matching for One-Step Visual Generation.

Three-Body Scattering for Generative Modeling Representation Distribution Matching for One-Step Visual Generation

Reference 14

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source=arxiv_source observed=2026-08-01T15:49:30.786732Z digest=sha256:9c358daa5b5113040578f87a834a22bd21406babeaabdea473c9a4457c787909

Observation e5f907b5-8200-4759-bea9-34011633ed4d · outbound

This paper cites One Step Diffusion via Shortcut Models.

Three-Body Scattering for Generative Modeling One Step Diffusion via Shortcut Models

Reference 15

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source=arxiv_source observed=2026-08-01T15:49:30.855734Z digest=sha256:49f3f7e997334ac8fd9ecd0dfc1d27ec546a47b75b319aa9ccdb6071653c9f36

Observation f4272f96-2c9f-4884-9e3b-ea75c32a9330 · outbound

This paper cites Drifting Fields are not Conservative.

Three-Body Scattering for Generative Modeling Drifting Fields are not Conservative

Reference 16

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source=arxiv_source observed=2026-08-01T15:49:30.927805Z digest=sha256:c2c8daf3907a341444d2bea31de0bbb1be0b0776d4c600be0e46c715a75dad79

Observation 1f177e75-2fe0-4da0-b7fb-9a71581daf0f · outbound

This paper cites Deep MMD gradient flow without adversarial training.

Three-Body Scattering for Generative Modeling Deep MMD gradient flow without adversarial training

Reference 17

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source=arxiv_source observed=2026-08-01T15:49:30.994729Z digest=sha256:2658b5a08cc0f32d35a0ba97d5c8f5a330297abbd6f70e9e138f9e10dc20ac62

Observation ea75867d-2403-4988-b5d0-988ad1673f3c · outbound

This paper cites Mean Flows for One-step Generative Modeling.

Three-Body Scattering for Generative Modeling Mean Flows for One-step Generative Modeling

Reference 18

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source=arxiv_source observed=2026-08-01T15:49:31.074582Z digest=sha256:c08bf5fcc3c6247eebd6c48f3f93ba024b00058b6d54642c2011e4282aef8006

Observation fe725b25-7748-4020-a726-f1ec8ab5288d · outbound

This paper cites Generative adversarial nets.

Three-Body Scattering for Generative Modeling Generative adversarial nets

Reference 19

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source=arxiv_source observed=2026-08-01T15:49:31.152241Z digest=sha256:271a281f462f8c314dd54fa56ef53d036bac50e77e5e886f3b13bd1dd2c718e4

Observation 6c9934f7-bd00-4a10-972c-70d3ac3dd995 · outbound

This paper cites Borgwardt, Malte J.

Three-Body Scattering for Generative Modeling Borgwardt, Malte J

Reference 20

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source=arxiv_source observed=2026-08-01T15:49:31.239029Z digest=sha256:8911da37f246e93beda16063fb8cf080b1ad34c90bb2132c7c48c149d91e6ca2

Observation d19afa76-72e5-4f0b-bba9-4c2d2df47518 · outbound

This paper cites Posterior sampling based on gradient flows of the MMD with negative distance kernel.

Three-Body Scattering for Generative Modeling Posterior sampling based on gradient flows of the MMD with negative distance kernel

Reference 21

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source=arxiv_source observed=2026-08-01T15:49:31.290678Z digest=sha256:725f78b000e223f4aa195ffb8ff3ea3275802c10b33c6db2770ad6c5cbb9e368

Observation 429d449a-5a27-465c-b11e-3378a7863174 · outbound

This paper cites One-Step Generative Modeling via Wasserstein Gradient Flows.

Three-Body Scattering for Generative Modeling One-Step Generative Modeling via Wasserstein Gradient Flows

Reference 22

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source=arxiv_source observed=2026-08-01T15:49:31.296099Z digest=sha256:ba13c76399f3f76b3e4701e6f053c092766e6951a4cc489fa97b994e2214727b

Observation 5159f575-9a58-433c-bb23-e82455296b84 · outbound

This paper cites Deep residual learning for image recognition.

Three-Body Scattering for Generative Modeling Deep residual learning for image recognition

Reference 23

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source=arxiv_source observed=2026-08-01T15:49:31.375466Z digest=sha256:a56dc5a32510fa4f4253ebaacbffc51d057366f4a1bd36a4a1daf1a29d94a538

Observation c06b815c-9ce8-48fb-8a83-0c6ad02a4cd6 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Three-Body Scattering for Generative Modeling Masked autoencoders are scalable vision learners

Reference 24

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source=arxiv_source observed=2026-08-01T15:49:31.457986Z digest=sha256:3bf41d1d0da309b6a9307b194bcb5cfe9b4926ec4c3eb63227968c9aea8730ff

Observation fb734492-c855-4366-85da-d892e43ad6ca · outbound

This paper cites Sinkhorn-drifting generative models.

Three-Body Scattering for Generative Modeling Sinkhorn-drifting generative models

Reference 25

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source=arxiv_source observed=2026-08-01T15:49:31.566526Z digest=sha256:27377ed055b746fb46e0587be14f2945314fe782174aba9a9a5bee0cbd5daf5b

Observation 16bfa92e-1700-420e-9e5e-708357e02cc3 · outbound

This paper cites Generative sliced MMD flows with Riesz kernels.

Three-Body Scattering for Generative Modeling Generative sliced MMD flows with Riesz kernels

Reference 26

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source=arxiv_source observed=2026-08-01T15:49:31.665700Z digest=sha256:7ec31aab9f802c62e47bbe738487c4c9fc6926f84e5ae9a901a0fbf34f5e3abe

Observation b1f45d6d-c899-4c52-9f51-3c56ef5b50dd · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Three-Body Scattering for Generative Modeling Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 27

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source=arxiv_source observed=2026-08-01T15:49:31.756713Z digest=sha256:dca365eef42e348211c5a87183e4ecb20db2a4043eb49123d7de31008caac56e

Observation 659aef47-b806-4ff5-8763-182404e671c5 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Three-Body Scattering for Generative Modeling Classifier-Free Diffusion Guidance

Reference 28

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source=arxiv_source observed=2026-08-01T15:49:31.808127Z digest=sha256:bdaf52a9611ffce9758492ea0da04ae522e349bf9e2c9255981663d402faaa7a

Observation 6693c23f-9d7d-459e-8975-5aa049b7954f · outbound

This paper cites Denoising diffusion probabilistic models.

Three-Body Scattering for Generative Modeling Denoising diffusion probabilistic models

Reference 29

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source=arxiv_source observed=2026-08-01T15:49:31.997730Z digest=sha256:a0089645e6bd0b8b3d84c9e51c367e9709ff381ca0e743cb172944901281435c

Observation ccf9e337-3c8a-45e2-9fc3-c5c6b104c80d · outbound

This paper cites The variational formulation of the Fokker--Planck equation.

Three-Body Scattering for Generative Modeling The variational formulation of the Fokker--Planck equation

Reference 30

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source=arxiv_source observed=2026-08-01T15:49:32.138794Z digest=sha256:01b3e7b9101c343603974dda8ef57d96cf72284fb9d012cd1b47fdebfe6d3a55

Observation be576122-0bf5-4efa-8b10-4d07b83e6a87 · outbound

This paper cites Scaling up gans for text-to-image synthesis.

Three-Body Scattering for Generative Modeling Scaling up gans for text-to-image synthesis

Reference 31

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source=arxiv_source observed=2026-08-01T15:49:32.235354Z digest=sha256:ac5f77ceb1c5142a5f85d173b5fffec5d866a2815205f10b8696bde819ec5e94

Observation f5962c68-9277-4497-b9d8-753f2255d814 · outbound

This paper cites Analyzing and improving the training dynamics of diffusion models.

Three-Body Scattering for Generative Modeling Analyzing and improving the training dynamics of diffusion models

Reference 32

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source=arxiv_source observed=2026-08-01T15:49:32.375344Z digest=sha256:35a2bf18f68e29601fc17f38b6282a3111b8dbadee1275a1e1108f46b77a8ec8

Observation 3b7a0675-5ab4-432b-8c00-f594de70ac87 · outbound

This paper cites Kingma and Ruiqi Gao.

Three-Body Scattering for Generative Modeling Kingma and Ruiqi Gao

Reference 33

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source=arxiv_source observed=2026-08-01T15:49:32.479373Z digest=sha256:0dd97d9c654ab6e3e3980adb2b8dfba870f4c7cabf97eca6b62cccc37f0629ab

Observation a3634ee5-5b8c-47a3-9a7a-c73ac184d924 · outbound

This paper cites MMD GAN : Towards deeper understanding of moment matching network.

Three-Body Scattering for Generative Modeling MMD GAN : Towards deeper understanding of moment matching network

Reference 34

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source=arxiv_source observed=2026-08-01T15:49:32.625686Z digest=sha256:8eaebbcdafc224ead1003094147a5997e48cd4342d8ce4d8785992d27f2653bc

Observation ab12ab41-8727-4a99-b4dc-0e65bd709164 · outbound

This paper cites Back to Basics: Let Denoising Generative Models Denoise.

Three-Body Scattering for Generative Modeling Back to Basics: Let Denoising Generative Models Denoise

Reference 35

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source=arxiv_source observed=2026-08-01T15:49:32.727026Z digest=sha256:bbad4c0faee8f6762f6d4dc2d463f2a2170c07d793d53a9e348fbbe24edd587d

Observation 2ca2683d-ddf9-402a-ab30-7e36440beaf6 · outbound

This paper cites Autoregressive image generation without vector quantization.

Three-Body Scattering for Generative Modeling Autoregressive image generation without vector quantization

Reference 36

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source=arxiv_source observed=2026-08-01T15:49:32.818807Z digest=sha256:82f8ab3932bdcc383e16cf1256733489f65635c9e4d44139edbff2549f3aa11d

Observation 83daf7ad-efc0-4b95-9472-04f7940bcca9 · outbound

This paper cites Generative moment matching networks.

Three-Body Scattering for Generative Modeling Generative moment matching networks

Reference 37

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source=arxiv_source observed=2026-08-01T15:49:32.933443Z digest=sha256:cd72580f1ac65d99fb50c04bfab363619b43cf3b82e6d308c8fb77c72b899824

Observation 3d41ca75-54b5-4626-901b-b335d8cc70e8 · outbound

This paper cites Adversarial Flow Models.

Three-Body Scattering for Generative Modeling Adversarial Flow Models

Reference 38

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source=arxiv_source observed=2026-08-01T15:49:33.103229Z digest=sha256:debc28a7eb52850fc5278fc3c287fadbc7ddc4a20240f835b92ed68917325482

Observation 7afd5666-29cd-4e42-aafb-4f6a6f28102c · outbound

This paper cites Continuous Adversarial Flow Models.

Three-Body Scattering for Generative Modeling Continuous Adversarial Flow Models

Reference 39

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source=arxiv_source observed=2026-08-01T15:49:33.201379Z digest=sha256:309d92939d2d5c59eb8675247b39578868388ca50b5d40e26d1b88571ba906b9

Observation 45d46997-d9e0-451e-8d97-3fa5a41a5226 · outbound

This paper cites Flow Matching for Generative Modeling.

Three-Body Scattering for Generative Modeling Flow Matching for Generative Modeling

Reference 40

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source=arxiv_source observed=2026-08-01T15:49:33.293568Z digest=sha256:14cdd4a573d64ab4e00c05590e7856b70fa902f286ef2db5a9bca6b4e37cced9

Observation 4661f259-22d4-42a6-81b9-fc13cf6f871c · outbound

This paper cites Stein variational gradient descent: A general purpose Bayesian inference algorithm.

Three-Body Scattering for Generative Modeling Stein variational gradient descent: A general purpose Bayesian inference algorithm

Reference 41

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source=arxiv_source observed=2026-08-01T15:49:33.391744Z digest=sha256:65e0c95ae4b6875c41dc89bbbf52eac5b467f57a5b7561d1ef16d286626c1392

Observation 5ce8d8fc-da6f-4a99-8907-fd2de7b646f9 · outbound

This paper cites Decoupled Weight Decay Regularization.

Three-Body Scattering for Generative Modeling Decoupled Weight Decay Regularization

Reference 42

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source=arxiv_source observed=2026-08-01T15:49:33.546332Z digest=sha256:1ecc66cfb5a0a7265acb07dfd2041e013c35c825fae19546acdcc06bfcbc11ee

Observation dbd3c3f7-2310-4bd0-b94c-cc6c50180cfb · outbound

This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

Three-Body Scattering for Generative Modeling Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 43

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source=arxiv_source observed=2026-08-01T15:49:33.618481Z digest=sha256:2fe2fb0a764e73ab65ea0599188210e953c3e2e3a3a2700407bec42ef08f62eb

Observation 1f671e5f-3611-46ae-a9fb-479e57ea4e50 · outbound

This paper cites One-step Latent-free Image Generation with Pixel Mean Flows.

Three-Body Scattering for Generative Modeling One-step Latent-free Image Generation with Pixel Mean Flows

Reference 44

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source=arxiv_source observed=2026-08-01T15:49:33.778334Z digest=sha256:5c1ca97b13772e55d3f8b84c990425ee1c875725c9ca5b20c19c5b021bdeb2c1

Observation 94e23bf8-a2c7-44d0-96bc-bdc70996a899 · outbound

This paper cites Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers.

Three-Body Scattering for Generative Modeling Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers

Reference 45

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source=arxiv_source observed=2026-08-01T15:49:33.851814Z digest=sha256:da6eb5fa4b0912f20f3672e9efca5f44a8f3e391069aee5d1b16a4411c436c2a

Observation 01ae601e-87d4-44d0-992c-0451fa722990 · outbound

This paper cites PixelGen: Improving Pixel Diffusion with Perceptual Supervision.

Three-Body Scattering for Generative Modeling PixelGen: Improving Pixel Diffusion with Perceptual Supervision

Reference 46

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no resolver link, observed 2026-08-01T15:49:33.988843Z

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source=arxiv_source observed=2026-08-01T15:49:33.988843Z digest=sha256:2f93648e67c0e8f9553db07b7aefca6516c33dfbacff185107e3eaf11cc4d023

Observation c141a2bc-3230-4a86-8060-d985bb91f9a7 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Three-Body Scattering for Generative Modeling PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 47

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source=arxiv_source observed=2026-08-01T15:49:34.092616Z digest=sha256:5c69972e0a4e3b356960ae7d4bf2dda764f68e733e10baef65eafe41b7927354

Observation 21d39d1c-bcbf-4625-9334-564115ed76cf · outbound

This paper cites Scalable diffusion models with transformers.

Three-Body Scattering for Generative Modeling Scalable diffusion models with transformers

Reference 48

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source=arxiv_source observed=2026-08-01T15:49:34.177978Z digest=sha256:2c245e75effaef84df057c795dff6e321a95917f362b4dd26f6161df08b8a318

Observation 0e5a19e8-2ef8-4738-91aa-1b3cff1d4039 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Three-Body Scattering for Generative Modeling High-resolution image synthesis with latent diffusion models

Reference 49

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source=arxiv_source observed=2026-08-01T15:49:34.182165Z digest=sha256:2ca603047ff89a55acdbdeda59bca74bde448f428db9788d3dfe2921e955dc89

Observation 9b1f96c7-651a-47c0-abc6-0f03b4839e5c · outbound

This paper cites Smoothed distance kernels for MMD s and applications in Wasserstein gradient flows.

Three-Body Scattering for Generative Modeling Smoothed distance kernels for MMD s and applications in Wasserstein gradient flows

Reference 50

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no resolver link, observed 2026-08-01T15:49:34.186308Z

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source=arxiv_source observed=2026-08-01T15:49:34.186308Z digest=sha256:ee9fc71f49dd07c3c5bde34c7cbe2507ae32217461562f20d95ffbea70945032

Observation bba08984-459c-4e28-a7c9-fe30b4184838 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Three-Body Scattering for Generative Modeling Progressive Distillation for Fast Sampling of Diffusion Models

Reference 51

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no resolver link, observed 2026-08-01T15:49:34.190195Z

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source=arxiv_source observed=2026-08-01T15:49:34.190195Z digest=sha256:372bd2fbf65ac8842d42b41ca60d4b6e4afca65344c9e0d38218130517f9c83c

Observation 5c4e9ace-54c5-460d-a362-9c138d62d5a2 · outbound

This paper cites Stylegan-xl: Scaling stylegan to large diverse datasets.

Three-Body Scattering for Generative Modeling Stylegan-xl: Scaling stylegan to large diverse datasets

Reference 52

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no resolver link, observed 2026-08-01T15:49:34.193964Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.193964Z digest=sha256:b834f4ea8fabf7726a954430b248f29962ea31fb0d497c3e3e1f0b9de6060fb7

Observation 206792f9-9838-4c4e-b910-70077b45f873 · outbound

This paper cites Schilling, Renming Song, and Zoran Vondra c ek.

Three-Body Scattering for Generative Modeling Schilling, Renming Song, and Zoran Vondra c ek

Reference 53

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source=arxiv_source observed=2026-08-01T15:49:34.198060Z digest=sha256:5d1a9d1b8bb4a9f9ee81a58e773acbc9f77b3c72bae65549b7ab7271f7043f8b

Observation 0b2a90ea-57f6-4c5c-990d-0cb66e883006 · outbound

This paper cites Equivalence of distance-based and RKHS -based statistics in hypothesis testing.

Three-Body Scattering for Generative Modeling Equivalence of distance-based and RKHS -based statistics in hypothesis testing

Reference 54

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.201750Z digest=sha256:fd2293a8f4c664d58779c02cea6c77af8393113d6e4573da0f628f5123ad5e53

Observation cc405d2f-af69-4473-9751-58fdfbcc4353 · outbound

This paper cites an unresolved cited work.

Three-Body Scattering for Generative Modeling Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-01T15:49:34.206256Z digest=sha256:bcc1292496c7259bf0c6bb01e500c7f4b7495544f98227add07b0c8c687f9ce5

Observation 8887d9a0-1eb9-43d5-8291-1cc621494bbe · outbound

This paper cites Denoising Diffusion Implicit Models.

Three-Body Scattering for Generative Modeling Denoising Diffusion Implicit Models

Reference 56

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source=arxiv_source observed=2026-08-01T15:49:34.210708Z digest=sha256:48bef8abe6fc78aff9d53bf1074386a1c32b5711166c1ccaefd5baf2f50c1a94

Observation a9d580eb-f15c-4769-bb9e-99b06223fcf8 · outbound

This paper cites Improved Techniques for Training Consistency Models.

Three-Body Scattering for Generative Modeling Improved Techniques for Training Consistency Models

Reference 57

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source=arxiv_source observed=2026-08-01T15:49:34.215127Z digest=sha256:da3b7b92417bc537c8c5d5c9e5177a3f978543ad93a9c1d4a40a578c0afa769c

Observation a970f0c2-ed27-4394-b8e8-0c83056d5d5e · outbound

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

Three-Body Scattering for Generative Modeling Score-Based Generative Modeling through Stochastic Differential Equations

Reference 58

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no resolver link, observed 2026-08-01T15:49:34.219289Z

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source=arxiv_source observed=2026-08-01T15:49:34.219289Z digest=sha256:63e6fe3e2bd1e74e390cbff73af5c4eb31493930e7cf98dec0a0c674d4d10201

Observation 5a466dc4-4461-4e52-a8d6-d89367bcd00c · outbound

This paper cites Consistency Models.

Three-Body Scattering for Generative Modeling Consistency Models

Reference 59

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no resolver link, observed 2026-08-01T15:49:34.223406Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.223406Z digest=sha256:f8dc2b8c492bbb81873cd083dde9510019826a096fbd8370cdc89391361e3683

Observation 5406b67b-68de-44da-a302-96e1d2d1827e · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Three-Body Scattering for Generative Modeling Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 60

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source=arxiv_source observed=2026-08-01T15:49:34.228521Z digest=sha256:0d02fcddcca7abb8a9370054766a23cb66473f734048f7a09293ab93d282a874

Observation 429aa4a3-ea4b-46be-b6f0-ba7e3a6cc9b4 · outbound

This paper cites Unified Continuous Generative Models.

Three-Body Scattering for Generative Modeling Unified Continuous Generative Models

Reference 61

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source=arxiv_source observed=2026-08-01T15:49:34.306071Z digest=sha256:c873c1d6ee31a5337ebac9b995f0deb9d39bdfb7a8e4c0398becd7aebe558bf9

Observation fc54429a-de71-4fed-a0b7-92d5f30220c1 · outbound

This paper cites Sz \'e kely and Maria L.

Three-Body Scattering for Generative Modeling Sz \'e kely and Maria L

Reference 62

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source=arxiv_source observed=2026-08-01T15:49:34.456595Z digest=sha256:e1d6a20877955da3828fd9a54cacb3fe21db864fa99288a4ac5152756d459ff3

Observation 52cb2d76-4b94-4b66-8550-97d2f598d818 · outbound

This paper cites Sobolev Regularized MMD Gradient Flow.

Three-Body Scattering for Generative Modeling Sobolev Regularized MMD Gradient Flow

Reference 63

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source=arxiv_source observed=2026-08-01T15:49:34.619818Z digest=sha256:58145d9f30ec3844e17a7ed87f526d55281715686aa190e10059ba34cdf7be3a

Observation 0b1c38be-cee1-4ef9-a13d-ee68ba0e6a66 · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

Three-Body Scattering for Generative Modeling Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 64

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source=arxiv_source observed=2026-08-01T15:49:34.711292Z digest=sha256:2129226b827b89f1f8dad65dc9bfd4ceaeb5d44ec6c88071344310448183e46e

Observation fee3a09f-3f6d-4150-9ea2-92843a957405 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Three-Body Scattering for Generative Modeling SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 65

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source=arxiv_source observed=2026-08-01T15:49:34.715916Z digest=sha256:aa5f8cdd0f3b898269df1d230d106693ed67fc597019d5c3c15d71c52b7da76f

Observation 12d0a8d8-a3d9-42ec-bba1-afbb0244047a · outbound

This paper cites Coulomb GAN s: Provably optimal Nash equilibria via potential fields.

Three-Body Scattering for Generative Modeling Coulomb GAN s: Provably optimal Nash equilibria via potential fields

Reference 66

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source=arxiv_source observed=2026-08-01T15:49:34.720482Z digest=sha256:2057f6f5eb1e35fb226a097bd2aa0386a73f4521e6add39f5bfd9907d3b1e463

Observation a1475af2-ec3f-478c-8a63-a5ad2a05df1b · outbound

This paper cites Pixel recurrent neural networks.

Three-Body Scattering for Generative Modeling Pixel recurrent neural networks

Reference 67

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.724284Z digest=sha256:0406f3987e99afa6bb67b9f6a8fa8ba4cde389e05d48dde7a577e50c037997f0

Observation fbd68ca7-6a9f-4877-9546-2aeb4eebcb06 · outbound

This paper cites Qwen-Image Technical Report.

Three-Body Scattering for Generative Modeling Qwen-Image Technical Report

Reference 68

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:49:34.728546Z digest=sha256:6913375d2350266f465d5add954cb7f9a4faf6861622aa3d852730dac6d57c9c

Observation ce86e050-7fdc-42f4-993c-00f62f637faa · outbound

This paper cites Representation Fr\'echet Loss for Visual Generation.

Three-Body Scattering for Generative Modeling Representation Fr\'echet Loss for Visual Generation

Reference 69

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:49:34.732174Z digest=sha256:33954422f6d4e2f4ca1bfb18f8df3406d2fdc0fa27e6f5723e2660dc347cad5c

Observation b1f34e98-7fe0-4f6e-a4c2-610de4b5eb37 · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

Three-Body Scattering for Generative Modeling Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 70

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:49:34.736797Z digest=sha256:2ca1d37a8ceebea74963d54c94ef0cdd9d790d1a7724fcc1f8c8ddd443caf062

Observation ee5be9bd-143a-43e3-a427-19be5be822bd · outbound

This paper cites One-step diffusion with distribution matching distillation.

Three-Body Scattering for Generative Modeling One-step diffusion with distribution matching distillation

Reference 71

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.741006Z digest=sha256:fca6175ccdc2f3706d51efda9be95f4952f6bede2516a9ea4ff62c98cd529b78

Observation 168a649a-0a32-4b07-a546-4b80edc34ebd · outbound

This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

Three-Body Scattering for Generative Modeling Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 72

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no resolver link, observed 2026-08-01T15:49:34.744699Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.744699Z digest=sha256:c77c40330428cf2f4c63b287d0f562995a8e7ee77ce7528eed88d07a59c5da3a

Observation cd634990-6a11-426b-a0a1-3ac77557c246 · outbound

This paper cites Autoregressive image generation with masked bit modeling.

Three-Body Scattering for Generative Modeling Autoregressive image generation with masked bit modeling

Reference 73

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no resolver link, observed 2026-08-01T15:49:34.749153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T15:49:34.749153Z digest=sha256:b6c6f7ea1d7dee572b195b67d30886602d9dce34d6266c5f46afb2a41d338222

Observation 5d73881c-4a91-4238-b77b-18a54af97bb0 · outbound

This paper cites PixelDiT: Pixel Diffusion Transformers for Image Generation.

Three-Body Scattering for Generative Modeling PixelDiT: Pixel Diffusion Transformers for Image Generation

Reference 74

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T15:49:34.752933Z digest=sha256:ecd146e72fb4cbf70882c8c85321f0d0455a68e59863f303c1ff61a1024a9fb3

Observation cd57ed78-6004-4ce6-b964-dbe8057ba530 · outbound

This paper cites Sigmoid Loss for Language Image Pre-Training.

Three-Body Scattering for Generative Modeling Sigmoid Loss for Language Image Pre-Training

Reference 75

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source=arxiv_source observed=2026-08-01T15:49:34.756747Z digest=sha256:1f5978038a2cc12d416c2d9e8a6054926cb5a5ef1716b2d325ce0dc695651bb2

Observation 6eff6f55-d01b-4c38-8570-6935cb3d77f7 · outbound

This paper cites Perceptual Flow Matching for Few-Step Generative Modeling.

Three-Body Scattering for Generative Modeling Perceptual Flow Matching for Few-Step Generative Modeling

Reference 76

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source=arxiv_source observed=2026-08-01T15:49:34.760798Z digest=sha256:bfed089e7c86a5ba450781c322797bb0762788f1ac33aa525c6bf660de12f831

Pith citing papers

Observation f077ba27-c588-4ae8-9369-f93215a61928 · inbound

Amortized Moment Matching for Visual Generation cites this paper.

Amortized Moment Matching for Visual Generation Three-Body Scattering for Generative Modeling

Reference 160

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no resolver link, observed 2026-07-30T18:58:28.532532Z

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source=arxiv_source observed=2026-07-30T18:58:28.532532Z digest=sha256:c0438767bb05a7c967767b05321204a24a5fa5144e47f89343c79a218bb63038