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Paper Citation Record · LEDGER

Generative Distribution Distillation

As of 10 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 2 inbound Pith citation observations for arXiv:2507.14503.

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

pith.paper-citation-record.v1
2507.14503 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:10:22.241118Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T13:10:37.285667Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:03:56.729210Z

Reference resolution

59 of 59 outbound references displayed

  • verified exact0
  • verified fuzzy23
  • unresolved35
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 00289b22-9a01-4fce-9e55-b243a350392b · outbound

This paper cites write newline.

Generative Distribution Distillation write newline

Reference 1

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Observation e75a3e5a-5a75-4bf8-8afe-1fe0368fbfc5 · outbound

This paper cites GPT-4 Technical Report.

Generative Distribution Distillation GPT-4 Technical Report

Reference 2

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source=arxiv_source observed=2026-08-06T16:10:22.008628Z digest=sha256:2ff4248c393ee7dae1f6e479f5841c6846de97d4f3bef41953195a0dfe742732

Observation 92232be3-ae4b-4912-9060-de10d2f25e78 · outbound

This paper cites Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models.

Generative Distribution Distillation Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Reference 3

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Observation 6c15bad7-8834-4eba-a88b-9381ba73faee · outbound

This paper cites Language models are few-shot learners.

Generative Distribution Distillation Language models are few-shot learners

Reference 4

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Observation bea7b8f3-bfb6-4c2f-abdd-884232b29721 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Generative Distribution Distillation Learning imbalanced datasets with label-distribution-aware margin loss

Reference 5

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source=arxiv_source observed=2026-08-06T16:10:22.021410Z digest=sha256:c1894165e676b1a3fabb87f8ff586b831eadb20c0ee3fd2975447f5794e6fa63

Observation c9a3e461-a7c8-4daf-89e3-101fc97fb431 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Generative Distribution Distillation Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 6

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source=arxiv_source observed=2026-08-06T16:10:22.025224Z digest=sha256:3a1889c6fc86579786caaf9034bd4f1d3f65164b5f8aff726882bcf57aebf1a3

Observation 38b89597-0092-4e95-9ad1-3aa43af21e20 · outbound

This paper cites Distilling knowledge via knowledge review.

Generative Distribution Distillation Distilling knowledge via knowledge review

Reference 7

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source=arxiv_source observed=2026-08-06T16:10:22.029135Z digest=sha256:1611f47591a0f0600b85a23d25dc6dc4f0eb5ceba09df28d70d0ac4c4479f3c7

Observation 2c559846-f796-41e5-a87a-a98f8120ed8e · outbound

This paper cites On the efficacy of knowledge distillation.

Generative Distribution Distillation On the efficacy of knowledge distillation

Reference 8

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source=arxiv_source observed=2026-08-06T16:10:22.033377Z digest=sha256:446bff4c007c78f4d35fc47b8412d215f761642876e3f508b8560e380fb3a81a

Observation a3e3816c-b831-4764-a3fd-997e1cf27707 · outbound

This paper cites Parametric contrastive learning.

Generative Distribution Distillation Parametric contrastive learning

Reference 9

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source=arxiv_source observed=2026-08-06T16:10:22.037614Z digest=sha256:e0a53eae17e6637c95385a51635f9330a31b9c2143d0b671c779783bd5b7b3da

Observation af72f74d-c777-4fef-84f7-313a4d224f59 · outbound

This paper cites Reslt: Residual learning for long-tailed recognition.

Generative Distribution Distillation Reslt: Residual learning for long-tailed recognition

Reference 10

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source=arxiv_source observed=2026-08-06T16:10:22.042361Z digest=sha256:cb8f42d61512e9f94fe68d5559342f4ad257583be1af3e4c0e71c95c76ede5ae

Observation cb1e7e5c-3c98-4810-9a00-17c5e96b9d17 · outbound

This paper cites Generalized parametric contrastive learning.

Generative Distribution Distillation Generalized parametric contrastive learning

Reference 11

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source=arxiv_source observed=2026-08-06T16:10:22.046863Z digest=sha256:ab7f6af5d0bf584f358d8a6cfd4ebd70fdc133a71bf5d8025350aac408340e2b

Observation bbed58e5-729f-4d75-a157-d726204359a4 · outbound

This paper cites Decoupled kullback-leibler divergence loss.

Generative Distribution Distillation Decoupled kullback-leibler divergence loss

Reference 12

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.050403Z digest=sha256:6e5c014a3594b3ea890abe3efd590e7b6d6e43a9e7a1deb8b661e673b00598b7

Observation 7068f846-0d6e-4587-8529-0f0a2b191f41 · outbound

This paper cites Classes are not equal: An empirical study on image recognition fairness.

Generative Distribution Distillation Classes are not equal: An empirical study on image recognition fairness

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.054032Z digest=sha256:00b69468f36430326408d464b028ac5ca99c381f51b39ad1025c110c02c20fe6

Observation a3aa7c05-a9a2-46cd-980e-5f719d207598 · outbound

This paper cites Generalized Kullback-Leibler Divergence Loss.

Generative Distribution Distillation Generalized Kullback-Leibler Divergence Loss

Reference 14

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source=arxiv_source observed=2026-08-06T16:10:22.057881Z digest=sha256:aafbb93e3fed16ffd7bcb1758ec93a347fd0bff774595ae69e743255e2c839b6

Observation c036e058-5be9-49ed-be49-4d3ff8d0a571 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Generative Distribution Distillation Class-balanced loss based on effective number of samples

Reference 15

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source=arxiv_source observed=2026-08-06T16:10:22.061740Z digest=sha256:4da9886b0f42b0d86c5f9b90a180f6aad050c5882e9cc4d6b6905769a4924665

Observation e2c9e902-8dd8-4fa0-9d59-7507c1334178 · outbound

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

Generative Distribution Distillation Imagenet: A large-scale hierarchical image database

Reference 16

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source=arxiv_source observed=2026-08-06T16:10:22.065193Z digest=sha256:b899afb82ff922abc0d9d749528250b9ec44bff4d2c551a41e0bb01a11aa461e

Observation 3e050806-5d68-47c6-a051-591c3ae8a5e5 · outbound

This paper cites Unified Autoregressive Visual Generation and Understanding with Continuous Tokens.

Generative Distribution Distillation Unified Autoregressive Visual Generation and Understanding with Continuous Tokens

Reference 17

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source=arxiv_source observed=2026-08-06T16:10:22.068784Z digest=sha256:66d702084e54154b1847c811b38e00cb00152efadb17237f0ff51cb1ed37f57c

Observation 7ce6a014-960d-44e6-8e67-699c1f7bd170 · outbound

This paper cites Born again neural networks.

Generative Distribution Distillation Born again neural networks

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.072865Z digest=sha256:ac54202048cc8605d7afdd2855479a54c4c3fab459524a1ce256aeb6561b6bb7

Observation 1a0263c8-1df2-4af6-8423-cc57ee6ad496 · outbound

This paper cites Discrete flow matching.

Generative Distribution Distillation Discrete flow matching

Reference 19

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source=arxiv_source observed=2026-08-06T16:10:22.077223Z digest=sha256:4e0cc74df16f809815e7adb11e2eb4e904dd85d9071c4476f76c5566d4636cbe

Observation 006f0252-7cd0-4ee8-8d52-78adc277af6e · outbound

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

Generative Distribution Distillation Mean Flows for One-step Generative Modeling

Reference 20

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Observation 88a44338-3450-4ef8-ae08-fdebcd383331 · outbound

This paper cites VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale.

Generative Distribution Distillation VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale

Reference 21

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Observation 19a85b92-d408-4e48-8e06-8e1cc22b1b26 · outbound

This paper cites A comprehensive overhaul of feature distillation.

Generative Distribution Distillation A comprehensive overhaul of feature distillation

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8188e776-6479-4440-b267-84713b8ea9d3 · outbound

This paper cites Distilling the knowledge in a neural network.

Generative Distribution Distillation Distilling the knowledge in a neural network

Reference 23

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source=arxiv_source observed=2026-08-06T16:10:22.094240Z digest=sha256:459320adb341a22a57485bac3b4cc84cf7e94b1ab8036be343c3f468f30bad36

Observation fd7a3f07-4624-483c-b074-1f4fa2968962 · outbound

This paper cites Denoising diffusion probabilistic models.

Generative Distribution Distillation Denoising diffusion probabilistic models

Reference 24

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source=arxiv_source observed=2026-08-06T16:10:22.097806Z digest=sha256:aa2e76f40a05f1ddb4d5114a668b5141e7c0c1411ad5ed15e3041cfafc874604

Observation ec8ea7dd-be06-4e09-85f6-b5763bd811ba · outbound

This paper cites Knowledge distillation from a stronger teacher.

Generative Distribution Distillation Knowledge distillation from a stronger teacher

Reference 25

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source=arxiv_source observed=2026-08-06T16:10:22.101195Z digest=sha256:355eec94a317774acd2ea021cb62493b5524a1f51ee352538840980325f48f34

Observation a9a4a2a2-52d5-48d4-98e3-bf6f768257e8 · outbound

This paper cites Knowledge diffusion for distillation.

Generative Distribution Distillation Knowledge diffusion for distillation

Reference 26

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source=arxiv_source observed=2026-08-06T16:10:22.104694Z digest=sha256:8aa3ee00a1433dc2e72b0b7613d8573afec75505a84bb2fa00c750ebc4bf3d59

Observation 0ec7153e-f242-4d93-9ef7-0c14dc41a800 · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

Generative Distribution Distillation Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 27

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source=arxiv_source observed=2026-08-06T16:10:22.108808Z digest=sha256:6e582907287d8a6ca31b7d46cdb98b007aaf682de438a95e4a00f4acd2e0f1dc

Observation 1b041e38-2dcc-45e6-869c-9681aea10e5a · outbound

This paper cites Auto-encoding variational bayes, 2013.

Generative Distribution Distillation Auto-encoding variational bayes, 2013

Reference 28

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source=arxiv_source observed=2026-08-06T16:10:22.113263Z digest=sha256:d906f075fe0b97798946c6c198ff20718733f78395c178f83e7a30dd4cbd6b8c

Observation 214f586e-9b62-407b-a563-84a44a445fdb · outbound

This paper cites Learning multiple layers of features from tiny images.

Generative Distribution Distillation Learning multiple layers of features from tiny images

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9fa548b9-554e-4523-9622-1f5ab9e43d61 · outbound

This paper cites Learning multiple layers of features from tiny images.

Generative Distribution Distillation Learning multiple layers of features from tiny images

Reference 30

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source=arxiv_source observed=2026-08-06T16:10:22.122073Z digest=sha256:6c933d6f9b13f4d354546376870866e8deeafd087b115f909b39366bc95dd8e7

Observation 7f5dce79-1515-414e-8f72-053a2cfb79bb · outbound

This paper cites Autoregressive image generation without vector quantization.

Generative Distribution Distillation Autoregressive image generation without vector quantization

Reference 31

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raw_fallback, observed 2026-08-06T16:10:22.715411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.126506Z digest=sha256:657865cb7cc448edbf2a22742d2e5ee9453c1a0aa4496ea5daffd07f88a42d1e

Observation 1c8be4b6-91e9-4ad8-a6ce-16fcf63514da · outbound

This paper cites Flow Matching for Generative Modeling.

Generative Distribution Distillation Flow Matching for Generative Modeling

Reference 32

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source=arxiv_source observed=2026-08-06T16:10:22.130843Z digest=sha256:32b0fbd8c3c3bf8e2d3e0ffe13ba919dbe76f19d06d328e699ad9958969b79a6

Observation f6a1a124-cc38-44e4-b5ef-24bde7a1e720 · outbound

This paper cites Visual instruction tuning.

Generative Distribution Distillation Visual instruction tuning

Reference 33

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source=arxiv_source observed=2026-08-06T16:10:22.135732Z digest=sha256:6aa8bf0ef06ab9cd5106771158f008bcd8a34c41a67f3dd87c9966a30f1b9d3d

Observation f241c782-b1e0-425b-bf30-ad6221574c1d · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Generative Distribution Distillation Large-scale long-tailed recognition in an open world

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.140331Z digest=sha256:d5b87b4ce8ee0962dcbb76f79563530281c3bd6807d65e5057964abcced5caec

Observation b416ad32-b37e-4f61-9367-693f00d2a378 · outbound

This paper cites Wasserstein distance rivals kullback-leibler divergence for knowledge distillation.

Generative Distribution Distillation Wasserstein distance rivals kullback-leibler divergence for knowledge distillation

Reference 35

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raw_fallback, observed 2026-08-06T16:10:22.681697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.144802Z digest=sha256:158c34cd6d997ddf2d861afe4ab8431a5118c686527890cd49c96b6244072df7

Observation 793a43ec-dedc-4571-a3ef-88b76e5c22c8 · outbound

This paper cites Long-tail learning via logit adjustment.

Generative Distribution Distillation Long-tail learning via logit adjustment

Reference 36

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source=arxiv_source observed=2026-08-06T16:10:22.148947Z digest=sha256:819dc411ef34bc0c3b1a3476e4957af7e0a30fe77a003fd45b8b98f483413099

Observation 8edff2ae-a6be-4d4b-a334-11eec92b15c6 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Generative Distribution Distillation Improved denoising diffusion probabilistic models

Reference 37

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source=arxiv_source observed=2026-08-06T16:10:22.153518Z digest=sha256:8749cd3a69903a1011958b85b636fc14e8b859948eacd34415fcac14d6e52d9d

Observation 11687084-e6de-4d79-90c5-a0d3e5ac1e2d · outbound

This paper cites Training language models to follow instructions with human feedback.

Generative Distribution Distillation Training language models to follow instructions with human feedback

Reference 38

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source=arxiv_source observed=2026-08-06T16:10:22.157695Z digest=sha256:ee2613ac882966e04f29df5d87282b42ca203c95abc1b3c80b1069e92daa7241

Observation dbabca6d-069b-4a48-8611-ec504db73496 · outbound

This paper cites Relational knowledge distillation.

Generative Distribution Distillation Relational knowledge distillation

Reference 39

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raw_fallback, observed 2026-08-06T16:10:22.652884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.161480Z digest=sha256:b0e9c7ca4b80b3f2da46597a12e9ed9d1298b6e94652030490caf50a518a5381

Observation c6efe5a0-ae41-44b0-a7d5-3ccb31c3d847 · outbound

This paper cites Improving language understanding by generative pre-training.

Generative Distribution Distillation Improving language understanding by generative pre-training

Reference 40

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source=arxiv_source observed=2026-08-06T16:10:22.165154Z digest=sha256:e1c892df774cf8c0780b6a35e4c3bacfa946695a4952d4a12c3a0e3121a4979d

Observation 002596e8-6f15-40dd-a7c5-8e65454d629a · outbound

This paper cites Language models are unsupervised multitask learners.

Generative Distribution Distillation Language models are unsupervised multitask learners

Reference 41

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source=arxiv_source observed=2026-08-06T16:10:22.168579Z digest=sha256:8e867bc2b7370de9e2cca14d42625a380c024302e789f504f27e08f5a1a66d89

Observation a16ff439-282b-4042-af76-68bda21fd743 · outbound

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

Generative Distribution Distillation High-resolution image synthesis with latent diffusion models

Reference 42

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no resolver link, observed 2026-08-06T16:10:22.173215Z

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source=arxiv_source observed=2026-08-06T16:10:22.173215Z digest=sha256:5354840d500795d8ab914211ce9d90e43b1d13974d827ef7bdd0de7763507caf

Observation a8fd183a-1581-4d05-afed-8bf040627c11 · outbound

This paper cites Fitnets: Hints for thin deep nets.

Generative Distribution Distillation Fitnets: Hints for thin deep nets

Reference 43

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source=arxiv_source observed=2026-08-06T16:10:22.177835Z digest=sha256:b77bb10c9265fc313dbbcb5feed9ea801fee152eb9a5bbb6aee851d80dd559c1

Observation fd61dea6-788b-42bf-8dfc-c3e38ab2d0cf · outbound

This paper cites Image N et large scale visual recognition challenge.

Generative Distribution Distillation Image N et large scale visual recognition challenge

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.605528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.181677Z digest=sha256:2978996231e5e884a5b8930eba6b11f97b52e10549eb71e3da7ca6937a94b634

Observation c39baa72-671a-4e44-b30b-6d37219c993b · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Generative Distribution Distillation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 45

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no resolver link, observed 2026-08-06T16:10:22.185332Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.185332Z digest=sha256:3854c96f145d060de3754eeb9c543ac2bc41c219f2bc13667171b1885a3b5551

Observation 2da85518-a297-445b-a8b5-47aeeb926d64 · outbound

This paper cites Denoising Diffusion Implicit Models.

Generative Distribution Distillation Denoising Diffusion Implicit Models

Reference 46

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source=arxiv_source observed=2026-08-06T16:10:22.188899Z digest=sha256:254cc0f52260c6e4bc07ccfb96a911bb3c422b4707a63e7ad5484e0047ba5d19

Observation 62f5f4f6-24be-449f-ac5c-12d0bc37da7f · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Generative Distribution Distillation Generative modeling by estimating gradients of the data distribution

Reference 47

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.583872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.192795Z digest=sha256:f707b4af564cd0ae7de4b7b179e8f60fa8235e56e777bb43a54655a71b2882de

Observation 180062ff-4614-4e6f-83ac-0597f6a140fc · outbound

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

Generative Distribution Distillation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 48

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.196299Z digest=sha256:ac4946ae4ca060160639cc4ca3b82d74a53e81a2f8d1743abeb9226e74679430

Observation 5c7edff9-837a-4e9f-9bbc-efce08b68baf · outbound

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

Generative Distribution Distillation Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.569615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.200377Z digest=sha256:39e3192a90b53366395c1055a4fece5b411a2977ad28d79fcf4e814fde71e6d5

Observation fcd01fc7-515d-42a8-be76-441b8e4910e6 · outbound

This paper cites Contrastive representation distillation.

Generative Distribution Distillation Contrastive representation distillation

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.554212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.204036Z digest=sha256:4ca094b63fb4b24628b9702a67c7073dae7b5ceb6df5ae84cc212f599c728e6b

Observation c0d19935-d655-4c3f-8ad9-56f0e8c967df · outbound

This paper cites VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation.

Generative Distribution Distillation VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

Reference 51

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.207798Z digest=sha256:706ee89e125efab842e439342c2766d563bc17324ff03223bb10c28c36baf402

Observation 7caf39e7-b05f-461c-8c5f-f2a924b64ee5 · outbound

This paper cites MMaDA: Multimodal Large Diffusion Language Models.

Generative Distribution Distillation MMaDA: Multimodal Large Diffusion Language Models

Reference 52

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.212561Z digest=sha256:d0b9f207977a2326631789a0a066deabcd702b183d5f486433f06e176af28623

Observation 2ce37a5f-7b02-4b0d-b799-7080718b3840 · outbound

This paper cites Deep mutual learning.

Generative Distribution Distillation Deep mutual learning

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.539452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.217003Z digest=sha256:42c37dba3585ab781ec501bfdef62b3f90c9c72d14bfe9304b75525d7969d77a

Observation f683dcb1-7554-4b74-85c2-5a14e5498bee · outbound

This paper cites Decoupled knowledge distillation.

Generative Distribution Distillation Decoupled knowledge distillation

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.526901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.221371Z digest=sha256:aa5b43f8fc609a41e93f6a25e5e03dd539d47e335fd618e72d90aa4cf3c6b2f2

Observation 95675ddd-1f5c-4957-bbde-9da3c5876992 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

Generative Distribution Distillation Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 55

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source=arxiv_source observed=2026-08-06T16:10:22.225433Z digest=sha256:08f9ff36d90c7ac3439674078fdbf8b5f1c8f215d54dc745556ee286f5c6ed8b

Observation 5ecdfa42-a0b2-4026-a4b2-bef8e0b43399 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Generative Distribution Distillation MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 56

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source=arxiv_source observed=2026-08-06T16:10:22.229058Z digest=sha256:53293c1b8093b835ed8bc45feb2389a30d3456102bdca609e9f37118f57bbce9

Observation ff4f5492-4641-485b-b1bc-4151b36a044e · outbound

This paper cites @esa (Ref.

Generative Distribution Distillation @esa (Ref

Reference 57

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source=arxiv_source observed=2026-08-06T16:10:22.232728Z digest=sha256:37f2fef8d53efd358e5b0362a522124b5e8db02d871d6f64ae1195297d2089c9

Observation 37ee81bf-89d0-44b2-91a6-804f2d669a6b · outbound

This paper cites an unresolved cited work.

Generative Distribution Distillation Unresolved cited work

Reference 58

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unresolved
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source=arxiv_source observed=2026-08-06T16:10:22.236971Z digest=sha256:e52a9bb802972305f91c02d0b0b2bf8bf07f4daf81b17fa9b56fc9e006666000

Observation 0081f099-869b-4096-bdc2-e6650e38824c · outbound

This paper cites A naive GenDD baseline encounters two major challenges: the curse of high-dimensional optimization and the lack of semantic supervision from labels.

Generative Distribution Distillation A naive GenDD baseline encounters two major challenges: the curse of high-dimensional optimization and the lack of semantic supervision from labels

Reference 59

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malformed identifier
no resolver link, observed 2026-08-06T16:10:22.241118Z

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source=arxiv_source observed=2026-08-06T16:10:22.241118Z digest=sha256:d366b44db9b62617afd15df797a8ef18fd11ead6d0fc76b0983a14512c1707ec

Pith citing papers

Observation 43f35ce3-9858-4652-941e-00e68dc4085e · inbound

Class-frequency Guided Noise Schedule for Diffusion Models cites this paper.

Class-frequency Guided Noise Schedule for Diffusion Models Generative Distribution Distillation

Reference 29

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verified exact
arxiv_id, observed 2026-06-29T20:03:56.730958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T04:35:43.680865Z digest=sha256:448b3274979b79284a3f1c6632648bb39cedb0df2ef0a85f187500ed6f7595eb

Observation a25eee4c-821e-4e41-ac0d-395d08dabaec · inbound

Visual Token Compression Enhances Robustness of MLLMs cites this paper.

Visual Token Compression Enhances Robustness of MLLMs Generative Distribution Distillation

Reference 18

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source=pdf_text observed=2026-08-01T13:10:37.285667Z digest=sha256:c98c172b5420e6e8f48035a6c99ffb67627ca19fc74899f1c3a572bb79a9c893