Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-20T11:01:24.738195Z
Paper Citation Record · LEDGER
Vision Foundation Models as Generalist Tokenizers for Image Generation
As of 18 August 2026, this Paper Citation Record lists 100 of 104 outbound references and 1 inbound Pith citation observation for arXiv:2605.18390.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-10T07:31:26.225257Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T07:36:58.009410Z
Reference resolution
100 of 104 outbound references displayed
- verified exact38
- verified fuzzy56
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch6
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 4e9bae38-76a3-4f26-b64d-6a8f9dc687b5 · outbound
This paper cites Building Normalizing Flows with Stochastic Interpolants.
Vision Foundation Models as Generalist Tokenizers for Image Generation Building Normalizing Flows with Stochastic Interpolants
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.434134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9ae82e62-88a1-428b-a51c-052472268aac · outbound
This paper cites FlexTok: Resampling Images into 1D Token Sequences of Flexible Length.
Vision Foundation Models as Generalist Tokenizers for Image Generation FlexTok: Resampling Images into 1D Token Sequences of Flexible Length
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.436862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c661347a-3350-49e0-91cf-d1b0c1dadefb · outbound
This paper cites Autoencoders.
Vision Foundation Models as Generalist Tokenizers for Image Generation Autoencoders
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.293257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2e79dfd9-c309-4ec6-a2c8-d2073b2c8f41 · outbound
This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.419677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 64ab607e-1f3c-4490-87e5-35bf5614a5aa · outbound
This paper cites VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models.
Vision Foundation Models as Generalist Tokenizers for Image Generation VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.509258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0e330b87-a4c2-4292-8f06-e354d8e348be · outbound
This paper cites Understanding disentangling in $\beta$-VAE.
Vision Foundation Models as Generalist Tokenizers for Image Generation Understanding disentangling in $\beta$-VAE
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.499461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6531e696-8726-40cd-bc48-12cb9c65919e · outbound
This paper cites Emerging proper- ties in self-supervised vision transformers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Emerging proper- ties in self-supervised vision transformers
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.381692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 577fd82e-2723-4dcc-9376-5c734c9ba9f1 · outbound
This paper cites Maskgit: Masked generative image transformer.
Vision Foundation Models as Generalist Tokenizers for Image Generation Maskgit: Masked generative image transformer
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.369006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7f62fa5b-83c5-4d07-abe2-462a66b270e9 · outbound
This paper cites arXiv preprint arXiv:2509.25162 (2025) 4.
Vision Foundation Models as Generalist Tokenizers for Image Generation arXiv preprint arXiv:2509.25162 (2025) 4
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T11:03:13.446398Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 86b6e111-fbb5-4e10-820e-31a75b6bc23b · outbound
This paper cites Generative pretraining from pixels.
Vision Foundation Models as Generalist Tokenizers for Image Generation Generative pretraining from pixels
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.361169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ff4ac094-3b5e-49b8-8662-4d07118b6c23 · outbound
This paper cites Improved Baselines with Momentum Contrastive Learning.
Vision Foundation Models as Generalist Tokenizers for Image Generation Improved Baselines with Momentum Contrastive Learning
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.536627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3d8dbb5b-cc5f-4de4-8bf1-fdf35db38174 · outbound
This paper cites Detection in crowded scenes: One proposal, multiple predictions.
Vision Foundation Models as Generalist Tokenizers for Image Generation Detection in crowded scenes: One proposal, multiple predictions
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.331216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e26eeeb9-6e02-437e-ac6d-78c6afd63313 · outbound
This paper cites Deformable convolutional networks.
Vision Foundation Models as Generalist Tokenizers for Image Generation Deformable convolutional networks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.390167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b9bfbb4f-1db2-49bd-9bbc-7ca5cd2704d4 · outbound
This paper cites Vision Transformers Need Registers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Vision Transformers Need Registers
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.547925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation aeedf7ae-d146-4186-9207-db5d53c7a1ab · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
Vision Foundation Models as Generalist Tokenizers for Image Generation Imagenet: A large-scale hierarchical image database
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.312779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cf1cc1b3-746e-486f-a51d-46860a78ac60 · outbound
This paper cites Bert: Pre-training of deep bidirectional transform- ers for language understanding.
Vision Foundation Models as Generalist Tokenizers for Image Generation Bert: Pre-training of deep bidirectional transform- ers for language understanding
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.383783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fa51abb2-360b-4f8e-88ab-5d997fa4d3e5 · outbound
This paper cites Diffusion models beat gans on image synthesis.
Vision Foundation Models as Generalist Tokenizers for Image Generation Diffusion models beat gans on image synthesis
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.366414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4fb7f249-21f0-464f-882c-6328b9301c73 · outbound
This paper cites An introduction to variational autoencoders.
Vision Foundation Models as Generalist Tokenizers for Image Generation An introduction to variational autoencoders
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.392237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 26638286-bf8c-4de3-9fe1-611ef3610a89 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
Vision Foundation Models as Generalist Tokenizers for Image Generation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.518133Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2b4e0f94-ff25-42cb-a88a-fa90513818f2 · outbound
This paper cites Scaling rectified flow transformers for high- resolution image synthesis.
Vision Foundation Models as Generalist Tokenizers for Image Generation Scaling rectified flow transformers for high- resolution image synthesis
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.378602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6abe7458-1044-4e66-bb07-dacdc7e82c62 · outbound
This paper cites Taming transformers for high-resolution image synthesis.
Vision Foundation Models as Generalist Tokenizers for Image Generation Taming transformers for high-resolution image synthesis
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.377192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1f77c73d-7e69-4ecc-83ed-81eda16fc45b · outbound
This paper cites One layer is enough: Adapting pretrained visual encoders for image generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation One layer is enough: Adapting pretrained visual encoders for image generation
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.478362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8b4c9a63-dc23-436c-b93a-47a3c8d8824c · outbound
This paper cites Generative adversarial networks.
Vision Foundation Models as Generalist Tokenizers for Image Generation Generative adversarial networks
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.278967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7ef378e6-3f7d-4a45-9540-4a2dc0c8e1a4 · outbound
This paper cites Bootstrap your own latent-a new approach to self-supervised learning.
Vision Foundation Models as Generalist Tokenizers for Image Generation Bootstrap your own latent-a new approach to self-supervised learning
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.376255Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a0bbab4b-173f-4e72-9dca-99de3929f7dc · outbound
This paper cites Learnings from Scaling Visual Tokenizers for Reconstruction and Generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Learnings from Scaling Visual Tokenizers for Reconstruction and Generation
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.439682Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a16a26aa-333c-49e9-854b-f1be08784c49 · outbound
This paper cites Masked autoencoders are scalable vision learn- ers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Masked autoencoders are scalable vision learn- ers
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.363974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e56fbd35-ca4f-4507-9bc9-783a3df32da7 · outbound
This paper cites Momentum Contrast for Unsupervised Visual Representation Learning.
Vision Foundation Models as Generalist Tokenizers for Image Generation Momentum Contrast for Unsupervised Visual Representation Learning
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.423013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 019a7724-c873-49db-8112-7d6f1cf05ccf · outbound
This paper cites Deep residual learning for image recognition.
Vision Foundation Models as Generalist Tokenizers for Image Generation Deep residual learning for image recognition
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.349242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d90035ae-f1a2-4a67-92e1-67019c71184f · outbound
This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.
Vision Foundation Models as Generalist Tokenizers for Image Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.388477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6e09baef-298c-4181-b4e9-5b6504b1ba1d · outbound
This paper cites Burgess, Xavier Glorot, Matthew M.
Vision Foundation Models as Generalist Tokenizers for Image Generation Burgess, Xavier Glorot, Matthew M
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.381015Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0b37b325-454d-44e1-a563-a8013fd3d1d8 · outbound
This paper cites Denoising diffusion probabilistic models.
Vision Foundation Models as Generalist Tokenizers for Image Generation Denoising diffusion probabilistic models
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.303957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f0585fe7-5767-4c77-b329-bd31a01ef568 · outbound
This paper cites Image-to-image translation with conditional adversarial networks.
Vision Foundation Models as Generalist Tokenizers for Image Generation Image-to-image translation with conditional adversarial networks
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.339587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4f8e6445-fe22-44e2-bc70-80fa8f60ab3d · outbound
This paper cites Image-to-image translation with conditional adversarial networks.
Vision Foundation Models as Generalist Tokenizers for Image Generation Image-to-image translation with conditional adversarial networks
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.386054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6045b28b-f457-4b1b-8ed7-45394e3763fd · outbound
This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.
Vision Foundation Models as Generalist Tokenizers for Image Generation Scaling up visual and vision-language representation learning with noisy text supervision
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.394674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e6f2b59b-2ad7-4f60-b5fe-c988e04c6c37 · outbound
This paper cites Guiding a diffusion model with a bad version of itself.
Vision Foundation Models as Generalist Tokenizers for Image Generation Guiding a diffusion model with a bad version of itself
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.388264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 54e3dc9e-5f26-49ef-a986-f4649d371537 · outbound
This paper cites A style-based generator architecture for generative adversarial networks.
Vision Foundation Models as Generalist Tokenizers for Image Generation A style-based generator architecture for generative adversarial networks
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.379637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4a9803c4-5bfe-4788-8ace-3196d619838b · outbound
This paper cites Auto-Encoding Variational Bayes.
Vision Foundation Models as Generalist Tokenizers for Image Generation Auto-Encoding Variational Bayes
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.492647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c029ead1-642e-473d-8cc9-581b4d1971e7 · outbound
This paper cites EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling.
Vision Foundation Models as Generalist Tokenizers for Image Generation EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.461031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation daf38ee2-127c-4aac-a920-8d83afc4b710 · outbound
This paper cites arXiv preprint arXiv:2504.16064 , year=.
Vision Foundation Models as Generalist Tokenizers for Image Generation arXiv preprint arXiv:2504.16064 , year=
Reference 40
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T11:03:13.528025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 21c00f1e-307c-4e1f-900c-12ff3fa1da0e · outbound
This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.
Vision Foundation Models as Generalist Tokenizers for Image Generation The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.375115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7df956ba-741f-44d6-9e2e-3a05b3bc4ab1 · outbound
This paper cites Improved precision and recall metric for assessing generative models.
Vision Foundation Models as Generalist Tokenizers for Image Generation Improved precision and recall metric for assessing generative models
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.370758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 54a38ccd-6ccb-43d5-b38a-bca635aaeadd · outbound
This paper cites Autoregressive image generation using residual quantization.
Vision Foundation Models as Generalist Tokenizers for Image Generation Autoregressive image generation using residual quantization
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:14.372801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8943d534-2bfb-4ca8-a019-d727ef7a622c · outbound
This paper cites Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.520976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dad91f52-f94d-41cf-9d64-0f721ae82904 · outbound
This paper cites Autoregressive Image Generation without Vector Quantization.
Vision Foundation Models as Generalist Tokenizers for Image Generation Autoregressive Image Generation without Vector Quantization
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.515389Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 35a3c3f3-f2dc-45c9-a751-5897e4c373f8 · outbound
This paper cites ImageFolder: Autoregressive Image Generation with Folded Tokens.
Vision Foundation Models as Generalist Tokenizers for Image Generation ImageFolder: Autoregressive Image Generation with Folded Tokens
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.505362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5acccd36-1bd9-4940-949d-0244ff1b5051 · outbound
This paper cites Feature pyramid networks for object detection.
Vision Foundation Models as Generalist Tokenizers for Image Generation Feature pyramid networks for object detection
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.319767Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d5aae5c8-23ec-46d4-92e2-49c1d50c9d0a · outbound
This paper cites Focal loss for dense object detection.
Vision Foundation Models as Generalist Tokenizers for Image Generation Focal loss for dense object detection
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.371277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2a2f07b2-511f-4463-9d9b-f517be4a0f6a · outbound
This paper cites Flow Matching for Generative Modeling.
Vision Foundation Models as Generalist Tokenizers for Image Generation Flow Matching for Generative Modeling
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.474843Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 22c21af5-a7c8-4c39-86d7-17f6d34aa6f1 · outbound
This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.
Vision Foundation Models as Generalist Tokenizers for Image Generation Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Reference 50
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.452207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 442793b9-5d8d-4c0b-a763-7028d8ec488a · outbound
This paper cites Decoupled Weight Decay Regularization.
Vision Foundation Models as Generalist Tokenizers for Image Generation Decoupled Weight Decay Regularization
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.442381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3ec1f911-62c2-4e81-9af7-34807fb6efb5 · outbound
This paper cites Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
Reference 52
- Resolution
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- arxiv_id, observed 2026-05-20T11:03:13.484061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ac649c53-4f79-42db-9414-97c75499300d · outbound
This paper cites Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.354139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ea9248bc-495a-4754-86c0-9445a46f9fd5 · outbound
This paper cites Finite Scalar Quantization: VQ-VAE Made Simple.
Vision Foundation Models as Generalist Tokenizers for Image Generation Finite Scalar Quantization: VQ-VAE Made Simple
Reference 54
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.533895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c1d8f6c1-2c4e-44db-917f-b19d81e6b7ca · outbound
This paper cites Conditional Generative Adversarial Nets.
Vision Foundation Models as Generalist Tokenizers for Image Generation Conditional Generative Adversarial Nets
Reference 55
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.463672Z
Source-reported events for the cited work
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Observation 28ed9a64-345d-4177-b51e-004c01d54576 · outbound
This paper cites One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression.
Vision Foundation Models as Generalist Tokenizers for Image Generation One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.539717Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 679b6030-91bb-4466-b6d5-635efde81828 · outbound
This paper cites Improved denois- ing diffusion probabilistic models.
Vision Foundation Models as Generalist Tokenizers for Image Generation Improved denois- ing diffusion probabilistic models
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.290062Z
Source-reported events for the cited work
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Observation 482d5ab3-81ca-4b87-871b-1a90bc3555b4 · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision.
Vision Foundation Models as Generalist Tokenizers for Image Generation DINOv2: Learning Robust Visual Features without Supervision
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.545158Z
Source-reported events for the cited work
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Observation 03df1eec-62d9-4851-8293-a9205af759c1 · outbound
This paper cites Scalable diffusion models with transformers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Scalable diffusion models with transformers
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.351517Z
Source-reported events for the cited work
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Observation ab1bc623-546d-41bc-9581-ddde19cc7ab6 · outbound
This paper cites TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.455436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 706f594e-e853-4a1b-bbb9-1029e9398825 · outbound
This paper cites Learning transferable visual models from natural language super- vision.
Vision Foundation Models as Generalist Tokenizers for Image Generation Learning transferable visual models from natural language super- vision
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.308567Z
Source-reported events for the cited work
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Observation 5f3a9e1e-5e47-41bc-9b62-e5e1fca80fb7 · outbound
This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.
Vision Foundation Models as Generalist Tokenizers for Image Generation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Reference 62
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.481008Z
Source-reported events for the cited work
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Observation a295e7ff-2403-452d-b365-6f7fe6d4121c · outbound
This paper cites Improving language understanding by generative pre-training.
Vision Foundation Models as Generalist Tokenizers for Image Generation Improving language understanding by generative pre-training
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.373837Z
Source-reported events for the cited work
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Observation 41b546d7-5b6d-44bd-b9c1-a05bee75204c · outbound
This paper cites Generating diverse high-fidelity images with vq-vae-2.
Vision Foundation Models as Generalist Tokenizers for Image Generation Generating diverse high-fidelity images with vq-vae-2
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.386066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3839fa64-2f4e-4618-85ef-7ee48050a721 · outbound
This paper cites High-resolution image synthesis with latent diffusion models.
Vision Foundation Models as Generalist Tokenizers for Image Generation High-resolution image synthesis with latent diffusion models
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.336515Z
Source-reported events for the cited work
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Observation 0148ab18-6fe6-454d-bc10-ca9f1d64189d · outbound
This paper cites Photorealistic text-to-image diffusion models with deep language understanding.
Vision Foundation Models as Generalist Tokenizers for Image Generation Photorealistic text-to-image diffusion models with deep language understanding
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.296277Z
Source-reported events for the cited work
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Observation 5b69732e-982d-40db-9847-e8e7ce214bfd · outbound
This paper cites Improved techniques for training gans.
Vision Foundation Models as Generalist Tokenizers for Image Generation Improved techniques for training gans
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.390939Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c79eb551-b8f4-470f-893d-aedf7ba3b0ca · outbound
This paper cites Improved techniques for training gans.
Vision Foundation Models as Generalist Tokenizers for Image Generation Improved techniques for training gans
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.347031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 38afef1b-136f-48a9-b668-02f7675e4f8d · outbound
This paper cites Denoising Diffusion Implicit Models.
Vision Foundation Models as Generalist Tokenizers for Image Generation Denoising Diffusion Implicit Models
Reference 69
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.530872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d7ce9777-833b-4ecf-bce9-29c32b809eb1 · outbound
This paper cites DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies.
Vision Foundation Models as Generalist Tokenizers for Image Generation DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
Reference 70
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.490028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8a6cc765-4bf0-43bd-99e5-aabdf14ca6b0 · outbound
This paper cites Roformer: Enhanced transformer with rotary position embedding.
Vision Foundation Models as Generalist Tokenizers for Image Generation Roformer: Enhanced transformer with rotary position embedding
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.317427Z
Source-reported events for the cited work
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Observation b5d0087d-6a23-48b5-8005-31327366d18d · outbound
This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
Reference 72
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.502671Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cea157d7-0fdc-4739-b4a2-12f7404084e4 · outbound
This paper cites Rethinking the inception architecture for computer vision.
Vision Foundation Models as Generalist Tokenizers for Image Generation Rethinking the inception architecture for computer vision
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.321992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 78470744-c66c-40b4-af5c-a17b0d24c63b · outbound
This paper cites Unilip: Adapting clip for unified multimodal understanding, generation and editing.
Vision Foundation Models as Generalist Tokenizers for Image Generation Unilip: Adapting clip for unified multimodal understanding, generation and editing
Reference 74
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.558830Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6a3834f3-bfc7-4c5b-bde3-a61da64e68a9 · outbound
This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.
Vision Foundation Models as Generalist Tokenizers for Image Generation Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Reference 75
- Resolution
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- arxiv_id, observed 2026-05-20T11:03:13.431357Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e31dac73-9b9c-4d41-ad24-aa928e3d3772 · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
Vision Foundation Models as Generalist Tokenizers for Image Generation LLaMA: Open and Efficient Foundation Language Models
Reference 76
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.542703Z
Source-reported events for the cited work
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Observation 67f30945-4866-4694-97f0-29fd7f691ae1 · outbound
This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.
Vision Foundation Models as Generalist Tokenizers for Image Generation SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
Reference 77
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.512117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 11e29742-d5f7-4b76-9def-091f416dd664 · outbound
This paper cites Conditional 15 image generation with pixelcnn decoders.
Vision Foundation Models as Generalist Tokenizers for Image Generation Conditional 15 image generation with pixelcnn decoders
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.301488Z
Source-reported events for the cited work
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Observation 0eac972c-3aed-4f66-9646-7ab550a8fd74 · outbound
This paper cites Neural discrete representation learning.
Vision Foundation Models as Generalist Tokenizers for Image Generation Neural discrete representation learning
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.324322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 44d42f76-37e2-4ac2-8f12-91e3dba13773 · outbound
This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.
Vision Foundation Models as Generalist Tokenizers for Image Generation Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.393352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9687e62e-6039-45ed-8208-77a743a3694c · outbound
This paper cites DDT: Decoupled Diffusion Transformer.
Vision Foundation Models as Generalist Tokenizers for Image Generation DDT: Decoupled Diffusion Transformer
Reference 81
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.550688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 002edb18-0621-4af6-8e8b-d724b4c6b2f1 · outbound
This paper cites Image quality assessment: from error visibility to structural similarity.
Vision Foundation Models as Generalist Tokenizers for Image Generation Image quality assessment: from error visibility to structural similarity
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.283521Z
Source-reported events for the cited work
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Observation 4425373c-e46b-4ac9-95ca-178df2ce92c3 · outbound
This paper cites "Principal Components" Enable A New Language of Images.
Vision Foundation Models as Generalist Tokenizers for Image Generation "Principal Components" Enable A New Language of Images
Reference 83
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T11:03:13.555929Z
Source-reported events for the cited work
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Observation d9679bc2-2704-4bca-ae8e-297e3a91043f · outbound
This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
Reference 84
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.496321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 883240f2-704e-47e5-a3a7-90a814ab2cec · outbound
This paper cites Gigatok: Scaling visual tokenizers to 3 billion param- eters for autoregressive image generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Gigatok: Scaling visual tokenizers to 3 billion param- eters for autoregressive image generation
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.326472Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6e6f928f-0a8b-4c01-bfd3-f99235afa6cb · outbound
This paper cites Fasterdit: Towards faster diffusion transformers training with- out architecture modification.
Vision Foundation Models as Generalist Tokenizers for Image Generation Fasterdit: Towards faster diffusion transformers training with- out architecture modification
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.315138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 578ff45d-f513-48ad-aa24-688b1b80a3d8 · outbound
This paper cites Reconstruction vs.
Vision Foundation Models as Generalist Tokenizers for Image Generation Reconstruction vs
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.287261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9f53fd8b-a68e-41da-ab0b-ec1eccde035d · outbound
This paper cites Vector-quantized Image Modeling with Improved VQGAN.
Vision Foundation Models as Generalist Tokenizers for Image Generation Vector-quantized Image Modeling with Improved VQGAN
Reference 88
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.458098Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 96c3b084-3c8b-484b-8aa4-572b660b0c83 · outbound
This paper cites Scaling autoregressive models for content-rich text-to-image generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Scaling autoregressive models for content-rich text-to-image generation
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.333772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9915826e-ee5a-43c4-91d0-4356c29ed97c · outbound
This paper cites Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, and Lu Jiang.
Vision Foundation Models as Generalist Tokenizers for Image Generation Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, and Lu Jiang
Reference 90
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.298985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e695229d-c8f4-48b9-a5e4-59aa7f00e9d9 · outbound
This paper cites Randomized autoregressive visual generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Randomized autoregressive visual generation
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.356374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation df817617-febd-48ed-9a38-f9ca51fe0792 · outbound
This paper cites An Image is Worth 32 Tokens for Reconstruction and Generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation An Image is Worth 32 Tokens for Reconstruction and Generation
Reference 92
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T11:03:13.487184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f16ad447-eab2-429d-b721-9d57f3d2e685 · outbound
This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.
Vision Foundation Models as Generalist Tokenizers for Image Generation Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
Reference 93
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.466288Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b6592722-2279-4413-98d5-201a1398e249 · outbound
This paper cites Sigmoid loss for language image pre-training.
Vision Foundation Models as Generalist Tokenizers for Image Generation Sigmoid loss for language image pre-training
Reference 94
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.310543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d91df3cb-cce2-4775-9bed-e52ddb214be8 · outbound
This paper cites The unreasonable effectiveness of deep features as a perceptual metric.
Vision Foundation Models as Generalist Tokenizers for Image Generation The unreasonable effectiveness of deep features as a perceptual metric
Reference 95
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.342145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c2180349-ac9d-45af-88c5-48f7968560c0 · outbound
This paper cites Holistic tokenizer for autoregressive image generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Holistic tokenizer for autoregressive image generation
Reference 96
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.358669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5f27f037-a6bf-41ad-ae26-5f5442484552 · outbound
This paper cites arXiv preprint arXiv:2507.08441 , year=.
Vision Foundation Models as Generalist Tokenizers for Image Generation arXiv preprint arXiv:2507.08441 , year=
Reference 97
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T11:03:13.469337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f40168af-01ef-4c09-9325-62c5c106458f · outbound
This paper cites Diffusion Transformers with Representation Autoencoders.
Vision Foundation Models as Generalist Tokenizers for Image Generation Diffusion Transformers with Representation Autoencoders
Reference 98
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T11:03:13.553297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 764c9b29-8e43-4f63-b9c6-4cd0fe1d0125 · outbound
This paper cites Movq: Modulating quantized vectors for high-fidelity image generation.
Vision Foundation Models as Generalist Tokenizers for Image Generation Movq: Modulating quantized vectors for high-fidelity image generation
Reference 99
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.306135Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 07539e4e-6a3f-4ae2-ae0c-00fbec1cf69b · outbound
This paper cites Fast Training of Diffusion Models with Masked Transformers.
Vision Foundation Models as Generalist Tokenizers for Image Generation Fast Training of Diffusion Models with Masked Transformers
Reference 100
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T11:03:13.472208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 985670ae-37ab-402a-a5c9-6117c06516b1 · outbound
This paper cites ibot: Image bert pre-training with online tokenizer.
Vision Foundation Models as Generalist Tokenizers for Image Generation ibot: Image bert pre-training with online tokenizer
Reference 101
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T11:03:26.383587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
Observation cc960685-3011-4161-b367-c7aa67f85770 · inbound
DeltaV: Thinking with Visual State Updates in Unified Large Multimodal Models cites this paper.
DeltaV: Thinking with Visual State Updates in Unified Large Multimodal Models Vision Foundation Models as Generalist Tokenizers for Image Generation
Reference 58
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-10T07:36:58.010915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.