Paper Citation Record · LEDGER

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion

As of 23 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 2 inbound Pith citation observations for arXiv:2512.16636.

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

pith.paper-citation-record.v1
2512.16636 v2

Coverage vector

measured 82 of 82 reference resolution

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measured 84 of 84 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T18:44:41.266768Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T22:37:26.197423Z

Reference resolution

82 of 82 outbound references displayed

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External citation measurements

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Outbound references

Observation 8d51d297-961c-4d5a-b948-7dcf36df3375 · outbound

This paper cites Build- ing normalizing flows with stochastic interpolants.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Build- ing normalizing flows with stochastic interpolants

Reference 1

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Observation 740f3d83-c388-4fde-bc6c-fac2ac651fe7 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Self-supervised learning from images with a joint-embedding predictive architecture

Reference 2

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Observation 5d3b47e5-3c5a-47ed-8540-9df9c775de71 · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion All are worth words: A vit backbone for diffusion models

Reference 3

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Observation 4ef941e5-d47b-4cd6-aebf-5064908bbeea · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion BEiT: BERT Pre-Training of Image Transformers

Reference 4

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Observation 0925a43a-4c5d-4e67-830c-2d1ca510c03b · outbound

This paper cites Springer, 2006.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Springer, 2006

Reference 5

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Unresolved cited work

Reference 6

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Observation 4e3e6ab1-18ff-4bd0-9210-5803fd9284ee · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Emerg- ing properties in self-supervised vision transformers

Reference 7

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Observation 0b855597-dbdd-4373-9433-b6114998bd80 · outbound

This paper cites VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models

Reference 8

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Observation 0df692d1-ebdd-4aec-9a44-5fdf0e33f67c · outbound

This paper cites Masked autoencoders are effective tokenizers for diffusion models.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Masked autoencoders are effective tokenizers for diffusion models

Reference 9

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Unresolved cited work

Reference 10

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Observation 765ac947-acb9-4d9d-a655-459b455833de · outbound

This paper cites A simple framework for contrastive learning of visual representations.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion A simple framework for contrastive learning of visual representations

Reference 11

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Observation 38b4a382-3d24-4336-ba68-b894a571a5ff · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Masked-attention mask transformer for universal image segmentation

Reference 12

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Observation 5ee3afee-7831-41c1-84b5-3ddc1e6fe160 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion The cityscapes dataset for semantic urban scene understanding

Reference 13

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Observation 6d39280f-8cca-445c-8d14-6362f986d115 · outbound

This paper cites Vision Transformers Need Registers.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Vision Transformers Need Registers

Reference 14

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Observation 830e8906-d038-437c-bb27-e97e45e5ac4c · outbound

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

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Imagenet: A large-scale hierarchical image database

Reference 15

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Observation 831c0bf7-8263-4350-8419-a493ef957dd3 · outbound

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

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Imagenet: A large-scale hierarchical image database

Reference 16

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Observation dc0efaa7-c803-49f2-b599-c9cce6202af4 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Diffusion models beat GANs on image synthesis

Reference 17

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Observation 7e3b743b-87cb-4d35-9cf7-4301d6adc096 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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Observation e3d926a7-1985-4081-add0-46666feb9b61 · outbound

This paper cites MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer

Reference 19

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Observation 1eb127c7-f2dc-4e72-9df3-bf502a68a76b · outbound

This paper cites Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey

Reference 20

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Observation f595f5e1-eda2-43f7-9ad6-7d0a2e82f3e9 · outbound

This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Unsupervised Representation Learning by Predicting Image Rotations

Reference 21

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Observation 867bf7e8-cc6a-478b-8b17-37de167d9ecb · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

Reference 22

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Observation d613d44d-fc55-48d6-baa3-571c8e5751c3 · outbound

This paper cites Deep residual learning for image recognition.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Deep residual learning for image recognition

Reference 23

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Observation b33094db-9d3e-4ce1-bda3-9021a8a69d7a · outbound

This paper cites Masked autoencoders are scalable vision learners.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Masked autoencoders are scalable vision learners

Reference 24

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Observation 2a3514cd-3e6d-42cd-9b30-4f187b12c0c8 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 25

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Observation 7423e23b-4851-4ee4-a20a-da708a05980b · outbound

This paper cites Classifier-Free Diffusion Guidance.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Classifier-Free Diffusion Guidance

Reference 26

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This paper cites What to hide from your students: Attention-guided masked image modeling.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion What to hide from your students: Attention-guided masked image modeling

Reference 27

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Observation abbe83ec-96f7-4122-abf5-2d12270b97a5 · outbound

This paper cites DINO-foresight: Looking into the future with DINO.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion DINO-foresight: Looking into the future with DINO

Reference 28

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Observation cc9ae058-6ac4-4faf-8ad0-5a8bcb780043 · outbound

This paper cites Kingma and Jimmy Ba.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Kingma and Jimmy Ba

Reference 29

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Observation 7f3aa2c0-879c-4803-90ee-bff983573830 · outbound

This paper cites Glow: Generative flow with invertible 1x1 convolutions.Advances in neural information processing systems, 31, 2018.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Glow: Generative flow with invertible 1x1 convolutions.Advances in neural information processing systems, 31, 2018

Reference 30

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Observation dddf15c4-4844-44fb-95bd-edf3ae39df8e · outbound

This paper cites Ilias: Instance-level image retrieval at scale.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Ilias: Instance-level image retrieval at scale

Reference 31

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Observation 58ff81c8-644e-4455-beb0-602c888233a6 · outbound

This paper cites EQ-V AE: Equivariance regularized latent space for improved generative image modeling.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion EQ-V AE: Equivariance regularized latent space for improved generative image modeling

Reference 32

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Observation 646ee487-6636-4796-9cf4-7bb97149cbeb · outbound

This paper cites Boosting generative image modeling via joint image-feature synthe- sis.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Boosting generative image modeling via joint image-feature synthe- sis

Reference 33

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Observation bda32be2-13e8-43d2-802b-b10b5aee0741 · outbound

This paper cites Improved precision and recall met- ric for assessing generative models.Advances in neural in- formation processing systems, 32, 2019.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Improved precision and recall met- ric for assessing generative models.Advances in neural in- formation processing systems, 32, 2019

Reference 34

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This paper cites Applying guidance in a limited interval improves sample and distribution quality in diffusion models.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Applying guidance in a limited interval improves sample and distribution quality in diffusion models

Reference 35

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This paper cites A path towards autonomous machine intelli- gence version 0.9.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion A path towards autonomous machine intelli- gence version 0.9

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion REPA-E: Unlocking V AE for end-to-end tuning with latent diffusion transform- ers

Reference 37

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This paper cites Autoregressive image generation without vec- tor quantization.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Autoregressive image generation without vec- tor quantization

Reference 38

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This paper cites Feature pyramid networks for object detection.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Feature pyramid networks for object detection

Reference 39

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Unresolved cited work

Reference 40

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This paper cites Fully convolutional networks for semantic segmentation.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Fully convolutional networks for semantic segmentation

Reference 41

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Decoupled weight decay regularization, 2019

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This paper cites Understanding Diffusion Models: A Unified Perspective.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Understanding Diffusion Models: A Unified Perspective

Reference 43

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Sit: Exploring flow and diffusion-based generative models with scalable in- terpolant transformers

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This paper cites FA V AE-effective frequency aware latent tok- enizer.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion FA V AE-effective frequency aware latent tok- enizer

Reference 45

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Generating Images with Sparse Representations

Reference 46

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Unsupervised learning of visual representations by solving jigsaw puzzles

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Representation Learning with Contrastive Predictive Coding

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Unresolved cited work

Reference 49

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Scalable diffusion models with transformers

Reference 50

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion MIT press, 2023

Reference 51

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Observation 39c04fba-be01-49a3-bf93-1c694caaa008 · outbound

This paper cites Attention, please! revisiting attentive probing for masked image modeling.arXiv preprint arXiv:2506.10178, 2025.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Attention, please! revisiting attentive probing for masked image modeling.arXiv preprint arXiv:2506.10178, 2025

Reference 52

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This paper cites Instance-level composed image retrieval.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Instance-level composed image retrieval

Reference 53

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Observation cec3c88a-4922-4bf3-9f6e-511f744c2372 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Learn- ing transferable visual models from natural language super- vision

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REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Vi- sion transformers for dense prediction

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Observation e4dd7215-5fb6-4470-9e52-88ad3ed5ee36 · outbound

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

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion High-resolution image syn- thesis with latent diffusion models

Reference 56

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Observation b8550e01-325e-40fb-a717-7fb57d15c600 · outbound

This paper cites Improved techniques for training gans.Advances in neural information processing systems, 29, 2016.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Improved techniques for training gans.Advances in neural information processing systems, 29, 2016

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Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:58.698806Z

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Observation 36f56eae-5487-4627-acda-717a8ef8667a · outbound

This paper cites Laion-5b: An open large-scale dataset for train- ing next generation image-text models.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Laion-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:58.781538Z

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Observation 1bc2edc0-3491-4a49-bbd9-fefcfe4e9f10 · outbound

This paper cites Latent diffusion model without variational autoen- coder.arXiv preprint arXiv:2510.15301, 2025.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Latent diffusion model without variational autoen- coder.arXiv preprint arXiv:2510.15301, 2025

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:58.865855Z

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Observation 2826694a-c553-4f1d-a17c-26d8a98f74ad · outbound

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

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Score-Based Generative Modeling through Stochastic Differential Equations

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:58.933909Z

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Observation f4ef9b11-aee0-4a9f-985a-a3db6fa5e477 · outbound

This paper cites Lposs: Label propagation over patches and pixels for open-vocabulary semantic segmentation.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Lposs: Label propagation over patches and pixels for open-vocabulary semantic segmentation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.096212Z

Source-reported events for the cited work

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Observation f6feec5e-e1c1-4a44-ba4d-2d2581dbd1a8 · outbound

This paper cites Any-to-any generation via composable diffu- sion.Advances in Neural Information Processing Systems, 36:16083–16099, 2023.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Any-to-any generation via composable diffu- sion.Advances in Neural Information Processing Systems, 36:16083–16099, 2023

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.174630Z

Source-reported events for the cited work

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Observation d9c1e3ad-5dd4-4956-8844-f9f8c7b7c7f7 · outbound

This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.272712Z

Source-reported events for the cited work

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Observation 154f047f-7c10-4bf2-a929-ca75057ddd02 · outbound

This paper cites Probabilistic principal component analysis.Journal of the Royal Statis- tical Society Series B: Statistical Methodology, 61(3):611– 622, 1999.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Probabilistic principal component analysis.Journal of the Royal Statis- tical Society Series B: Statistical Methodology, 61(3):611– 622, 1999

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.390222Z

Source-reported events for the cited work

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Observation fd00661a-60e2-4ed7-b5be-a8f3d280c7ec · outbound

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

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.504779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe80813-3054-47a8-9d57-4acccafd8e1d · outbound

This paper cites DDT: Decoupled Diffusion Transformer.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion DDT: Decoupled Diffusion Transformer

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.622377Z

Source-reported events for the cited work

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Observation 301f9d20-2437-4db3-917f-9c8d77688e82 · outbound

This paper cites Representation entanglement for genera- tion: Training diffusion transformers is much easier than you think.NeurIPS, 2025.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Representation entanglement for genera- tion: Training diffusion transformers is much easier than you think.NeurIPS, 2025

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.769892Z

Source-reported events for the cited work

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Observation ba2e7e85-ec4f-4a9d-b8a0-daba1ea59643 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Depth anything: Unleashing the power of large-scale unlabeled data

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.828093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d7df3b8-99cf-4e2f-a3f0-831475bc2f2f · outbound

This paper cites Depth any- thing v2.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Depth any- thing v2

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.911168Z

Source-reported events for the cited work

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Observation bbf4a96c-f96b-400b-92d2-58b6d0044ed7 · outbound

This paper cites Fasterdit: Towards faster diffusion transformers train- ing without architecture modification.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Fasterdit: Towards faster diffusion transformers train- ing without architecture modification

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-03T15:34:59.984445Z

Source-reported events for the cited work

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Observation b083a7d9-7f04-406d-8735-4ec2a6424ed3 · outbound

This paper cites Reconstruc- tion vs.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Reconstruc- tion vs

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.077402Z

Source-reported events for the cited work

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Observation 79381013-a41a-4a23-8fd4-77f698f68b62 · outbound

This paper cites Language model beats diffusion - tokenizer is key to visual generation.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Language model beats diffusion - tokenizer is key to visual generation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.152346Z

Source-reported events for the cited work

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Observation 3c637ea7-8c78-4d7e-848e-57f7af0b784b · outbound

This paper cites Representation alignment for generation: Training diffusion transformers is easier than you think.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Representation alignment for generation: Training diffusion transformers is easier than you think

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.217223Z

Source-reported events for the cited work

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Observation fbf32576-f097-4772-a54a-c2a9e322c91e · outbound

This paper cites Dif- fusion models with deterministic normalizing flow priors.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Dif- fusion models with deterministic normalizing flow priors

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.307953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9afd10fa-b67c-4980-90ff-bf87760c4daf · outbound

This paper cites Language- guided image tokenization for generation.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Language- guided image tokenization for generation

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.426722Z

Source-reported events for the cited work

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Observation 907b6058-a0be-4ebb-bfd3-579dbe61de7c · outbound

This paper cites Normalizing flows are capable generative models.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Normalizing flows are capable generative models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.520342Z

Source-reported events for the cited work

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Observation 477ccf5b-d3e0-40a9-807d-c33083aeacef · outbound

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

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Sigmoid Loss for Language Image Pre- Training

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.570990Z

Source-reported events for the cited work

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Observation 0c936ab5-730b-4b42-81bd-a72c8ef6e648 · outbound

This paper cites Diffusion normalizing flow.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Diffusion normalizing flow

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.642201Z

Source-reported events for the cited work

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Observation 245d7d43-06ce-445d-b5b3-7af047c1b533 · outbound

This paper cites Pyramid scene parsing network.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Pyramid scene parsing network

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.737015Z

Source-reported events for the cited work

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Observation 7467a394-75b9-4e97-8669-c4ac14672611 · outbound

This paper cites Fast Training of Diffusion Models with Masked Transformers.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Fast Training of Diffusion Models with Masked Transformers

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.843984Z

Source-reported events for the cited work

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Observation 95b683dd-1660-45f8-8f20-d7dd704f7472 · outbound

This paper cites ibot: Image bert pre-training with online tokenizer.International Conference on Learning Representations (ICLR), 2022.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion ibot: Image bert pre-training with online tokenizer.International Conference on Learning Representations (ICLR), 2022

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-03T15:35:00.946674Z

Source-reported events for the cited work

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Observation 9297c7d9-ef4d-4ef7-8206-76e3fffa7352 · outbound

This paper cites Castle” (483) Class label = “Bald Eagle.

REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion Castle” (483) Class label = “Bald Eagle

Reference 82

Resolution: malformed identifier; no resolver link, observed 2026-08-03T15:35:01.028967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 39b32a4d-d54f-4a7d-84cc-83186cd6312d · inbound

Coevolving Representations in Joint Image-Feature Diffusion cites this paper.

Coevolving Representations in Joint Image-Feature Diffusion REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:33.395850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9a479675-650a-4298-936d-4f99a1f96992 · inbound

MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training cites this paper.

MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:33.395850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.