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Paper Citation Record · LEDGER

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation

As of 17 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2505.13439.

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

pith.paper-citation-record.v1
2505.13439 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:16:36.724968Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:27:00.510598Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T02:13:30.560857Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact0
  • verified fuzzy24
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation db410918-91ed-498b-880d-92c47a057137 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.300834Z digest=sha256:fdbacd20908d06aa84e1b53a410254c288cc7fe3d0162b293e346670435aa375

Observation 8904b23b-cdc1-4eb4-896f-f34b4f875235 · outbound

This paper cites Clifton, Yuxiong He, Dacheng Tao, and Shuaiwen Leon Song.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Clifton, Yuxiong He, Dacheng Tao, and Shuaiwen Leon Song

Reference 2

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.312359Z digest=sha256:11c44cc493ea98226bb12160ad157b543085ea17dfa17e8c80f8da1b1dae2f94

Observation 26325fd5-f7da-4ff6-990e-c4bb15145517 · outbound

This paper cites an unresolved cited work.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-15T20:16:36.319246Z digest=sha256:41e9cabc35c3f0a03578195de1b08277f65a79e066ae78e013cf1a7e08355259

Observation fb59921e-e5ca-4b5d-8f23-a7fac145d4f2 · outbound

This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Reference 5

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source=pdf_text observed=2026-08-15T20:16:36.342757Z digest=sha256:ba07abce455394372645a9404ba0f1c01ffab26d4ef01dea33e89ac22984e23b

Observation a5b652ff-d965-4adf-988f-0bda4ea9a5a2 · outbound

This paper cites ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation

Reference 6

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source=pdf_text observed=2026-08-15T20:16:36.354700Z digest=sha256:f5bc074f7cc5b026b6d8d663c8a78623337461eb6c72807e912574d61f193d8d

Observation b4eb62af-8856-46b6-93e7-991a6bca1028 · outbound

This paper cites Diffusion models in vision: A survey.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Diffusion models in vision: A survey

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.364010Z digest=sha256:4cabf8e16c0d7998d392841bbdd4244a00fbfb7c3b7da2faf4aad8f86f03b4c8

Observation 538b30ab-20d2-416e-99bd-ef3bb6787e56 · outbound

This paper cites DeepSeek-V3 Technical Report.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation DeepSeek-V3 Technical Report

Reference 8

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source=pdf_text observed=2026-08-15T20:16:36.370349Z digest=sha256:2445181f227d0d058feac255107c3bf2d35fb62e4a510005f2a3c6b6c3835e89

Observation 70d69441-ae04-4793-988e-ca469d293cab · outbound

This paper cites Taming transformers for high-resolution image synthesis.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Taming transformers for high-resolution image synthesis

Reference 9

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.377341Z digest=sha256:653cfacc11edc85a7622e87f45efc2e79f92ed24021e0cf84bc4112377ce0b5a

Observation f45c5ba9-0e6c-4fb9-8f9e-859ad9aad8af · outbound

This paper cites Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens

Reference 10

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source=pdf_text observed=2026-08-15T20:16:36.384466Z digest=sha256:5216daa29f83c464b250587dea854f26c63c52fd91e56554a9ee9cc07e48fb0a

Observation 1b0cb649-3dc8-4e6e-87d6-6df936d3e7e7 · outbound

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

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Unified Autoregressive Visual Generation and Understanding with Continuous Tokens

Reference 11

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source=pdf_text observed=2026-08-15T20:16:36.394421Z digest=sha256:4601e25767ec0863e7cd0853e17bc8ea0ba77c3dec9a9c8c35d7540305b4ae85

Observation e7820c31-5e22-45cb-a92c-e411abf5de04 · outbound

This paper cites SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation

Reference 12

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source=pdf_text observed=2026-08-15T20:16:36.401027Z digest=sha256:b566c18ff41395722c59abe0d080519fff118899b4c547ad3540ef94488e4ca3

Observation affc3f69-1212-4306-b807-b4b73f4a4a68 · outbound

This paper cites Geneval: An object-focused frame- work for evaluating text-to-image alignment.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Geneval: An object-focused frame- work for evaluating text-to-image alignment

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.409214Z digest=sha256:03d00d8c15cf408f44ef554e402eca5f5d17aab6c109d9b99b34e05a77fc1c4e

Observation 702514c5-fc94-4f91-a9ab-61fe4b3adfe7 · outbound

This paper cites Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Reference 14

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source=pdf_text observed=2026-08-15T20:16:36.417004Z digest=sha256:526f2d768212456d651402e7013c97da31b529bca88964ab4be7bfe6173fe91b

Observation d9cbcffc-f756-45e3-ac1b-ea60dcd642a6 · outbound

This paper cites Denoising diffusion probabilistic models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Denoising diffusion probabilistic models

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.422755Z digest=sha256:ff2d8b4326435fddbb6ca69474b6b1ba860b349c749cab42df447674d45245a8

Observation e8d033b2-051f-4aca-9fb9-ed536b844fdd · outbound

This paper cites T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.428618Z digest=sha256:a465df420cc14c44756502344aa4a60d0834d1b0224bf6b0c4f04cfcc90e1be0

Observation e6337c26-64a4-4e4d-8f0a-52d9de4633a1 · outbound

This paper cites T2i- compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation T2i- compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.433188Z digest=sha256:9a3e9049791adef2893e5761948ec975a29c2189b2711319d051faff7fe87935

Observation b6ea2a2f-7d13-4495-917d-5352094c9e3a · outbound

This paper cites Mixtral of Experts.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Mixtral of Experts

Reference 18

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source=pdf_text observed=2026-08-15T20:16:36.437691Z digest=sha256:63f44464ddd4f84ad56acc46a57f48a617eeead03980e440ee8bf814fa229677

Observation 617f1c81-8d5e-422f-81ca-1804817f5d68 · outbound

This paper cites Gemma 3 Technical Report.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Gemma 3 Technical Report

Reference 19

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source=pdf_text observed=2026-08-15T20:16:36.443765Z digest=sha256:974553d6f2fdfbc6087866df810afcf85dcb3020d35b0bf3a7359fa92ec0f6be

Observation ce6db9e8-273a-42be-8483-3aa33e23bce9 · outbound

This paper cites an unresolved cited work.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-15T20:16:36.452476Z digest=sha256:b0340c0cfc6720559479a5efc6f6147afb177ed6ed57e0efd1d81ed7ebfe5743

Observation 3dca018f-35e6-4697-80dd-f60765f49149 · outbound

This paper cites Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability

Reference 21

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source=pdf_text observed=2026-08-15T20:16:36.458745Z digest=sha256:8c9942c3113b104a2f3845a86a69538e7c1b44f62ea8117d29db0698a2051e01

Observation a1b5da0e-e809-4a95-810e-2af0c068cc6b · outbound

This paper cites Autoregressive image generation without vector quantization.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Autoregressive image generation without vector quantization

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.466766Z digest=sha256:98723917c7b61b5b1b4899e9ca83a17f4440a65b0f91f3084ba9c91f3194059b

Observation 135a407e-4e75-4019-902a-a41f79aba2e2 · outbound

This paper cites DMin: Scalable Training Data Influence Estimation for Diffusion Models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation DMin: Scalable Training Data Influence Estimation for Diffusion Models

Reference 23

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source=pdf_text observed=2026-08-15T20:16:36.473362Z digest=sha256:13bd16d7b7cf74f91182d0f40a63b80dbcb68fc2904aa82c750c582015d64524

Observation ea3a709d-62b7-4745-b640-8c06eef6a010 · outbound

This paper cites Token-wise influential training data retrieval for large language models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Token-wise influential training data retrieval for large language models

Reference 24

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.480095Z digest=sha256:4ed2b01e254623f23db61fa379e30bbae566cb10c203b169ea00b6287b734246

Observation f1ab93dd-d49d-4ce0-89b6-a50182fb56cf · outbound

This paper cites UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models

Reference 25

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source=pdf_text observed=2026-08-15T20:16:36.487502Z digest=sha256:03f0189309f0840dfc90baa7971d89ff75bdca029c550a65ac1030b41c35dc06

Observation 05103f85-38f4-4ae8-ac6c-8141a6e11884 · outbound

This paper cites Unified-io 2: Scaling autoregressive multimodal models with vision, language, audio, and action.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Unified-io 2: Scaling autoregressive multimodal models with vision, language, audio, and action

Reference 26

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.493103Z digest=sha256:35d2352c2725c2631c2ae4b1811028dc38a0a758f8d48bfefaae24617c682fcb

Observation 5d2824b9-b490-4ea5-9cd5-26d9bd75015a · outbound

This paper cites Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 27

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source=pdf_text observed=2026-08-15T20:16:36.500770Z digest=sha256:c10ddbeb1c0c7fbc0764f53b98c3dde5bc0ba99a61713be541a354fce0130ead

Observation f83de64b-06dd-478d-bd12-388f9ba8b9ae · outbound

This paper cites Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

Reference 28

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source=pdf_text observed=2026-08-15T20:16:36.507549Z digest=sha256:7d31d52a9608c5577373978d02436cea92e9223bf7d4579bf40e766ad90c1ada

Observation d13a32ad-66c6-4e74-a2c8-081202648097 · outbound

This paper cites Blaschko, Guohao Dai, Huazhong Yang, and Yu Wang.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Blaschko, Guohao Dai, Huazhong Yang, and Yu Wang

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.513880Z digest=sha256:275f60dc62d795b500335931e86ecebd2a133188200db7af65ae33055185d747

Observation b30d8c8b-1591-4933-95b9-e2207f1105d7 · outbound

This paper cites Introducing 4o image generation, 2025.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Introducing 4o image generation, 2025

Reference 30

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source=pdf_text observed=2026-08-15T20:16:36.524284Z digest=sha256:bb433610b0bd83bb3a31a0c1c85550c92879ecd0a2f793d3ea8515d62f794196

Observation 9967247f-93ea-4f43-9020-89cb3ac4482b · outbound

This paper cites ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation

Reference 31

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source=pdf_text observed=2026-08-15T20:16:36.531312Z digest=sha256:2e5c42956be3095a14d99ea20f58b3121e9ba363ffca6b38d51b927c51b57b01

Observation 5ee96cbb-b416-476c-9567-5bb9f58cc224 · outbound

This paper cites Scalable diffusion models with transformers.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Scalable diffusion models with transformers

Reference 32

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.536456Z digest=sha256:cc56e6f1b2efeef16f8463d352c64a8df228769c430becd1c84f17da9b2b4bbe

Observation 8279ae23-be39-4867-b902-05795387f8f3 · outbound

This paper cites Reasoning with large language models, a survey.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Reasoning with large language models, a survey

Reference 33

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source=pdf_text observed=2026-08-15T20:16:36.542561Z digest=sha256:b357de31d05fff3f0d140c18719294ad44fa31cc2a3267245c466b99d274ba32

Observation a677e66f-0014-4f3e-aaaf-a910fedb4a03 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation High-Resolution Image Synthesis with Latent Diffusion Models

Reference 34

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source=pdf_text observed=2026-08-15T20:16:36.548934Z digest=sha256:f5bb23ca187a7d829fdb0e0d4d485b0123dbafc2316fbbbc985a8318b322a1aa

Observation f678be5b-2c8b-47f3-a3c0-6b5e9c09c1c8 · outbound

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

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation High- resolution image synthesis with latent diffusion models

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.557363Z digest=sha256:a326fee3bc4389c0e2e52c22f288dc507ca3bca2af0db808063ffcd706d0f309

Observation dcccecc1-3ad7-4d4c-becb-eac94a414194 · outbound

This paper cites Bernstein, Alexander C.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Bernstein, Alexander C

Reference 36

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.563815Z digest=sha256:960299dbbedd24718675a887fa3b5c8df0a61e37fd253156d41ff1a3bc8b7543

Observation 075afcea-125e-4910-ac84-9813d4aa96c9 · outbound

This paper cites Flow to the mode: Mode- seeking diffusion autoencoders for state-of-the-art image tokenization.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Flow to the mode: Mode- seeking diffusion autoencoders for state-of-the-art image tokenization

Reference 37

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source=pdf_text observed=2026-08-15T20:16:36.570432Z digest=sha256:5f4a778770aeaa58d65bf184be39804b3ca21099ed130038e67555fe1a6c0c85

Observation dedf71b8-daa2-4b2b-b43b-7ae23469a3f9 · outbound

This paper cites Drivelm: Driving with graph visual question answering.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Drivelm: Driving with graph visual question answering

Reference 38

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raw_fallback, observed 2026-08-15T20:16:37.857053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.580593Z digest=sha256:bc13738328a2c0999d0b05d9200f12627ee83057864c2022c97552e1620a06db

Observation 71b0f393-a913-400e-854b-6d81b7579d0c · outbound

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

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 39

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source=pdf_text observed=2026-08-15T20:16:36.588401Z digest=sha256:bbfa916df5e6a5ef0fe798822df793727596f0cd5f82b125f09a7abaeff345fa

Observation a23b861a-d57b-4505-9476-2a0a2abbb635 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 40

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source=pdf_text observed=2026-08-15T20:16:36.594006Z digest=sha256:17fa9a948926ad870a09fbd49ebb3e52ec3ba7d646c6a7853dbd65083fdc6703

Observation 75d46c6d-b531-4021-8991-219e7328869e · outbound

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

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 41

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raw_fallback, observed 2026-08-15T20:16:37.836930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.608247Z digest=sha256:069649d458b9c43a8c4779f0f41b014a13ab5ea51bfcd8ae64ca657a21c15908

Observation dd99bfe0-6c01-4926-90c7-7c4efe76a536 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation LLaMA: Open and Efficient Foundation Language Models

Reference 42

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source=pdf_text observed=2026-08-15T20:16:36.614723Z digest=sha256:a5fc49bb7bd6b83e3176f84331ffb7810fe8cf4d9586bd00f7ac25726925bef1

Observation 7d22ed68-bec4-4b7a-a314-a11af275c057 · outbound

This paper cites Neural discrete representation learning.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Neural discrete representation learning

Reference 43

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raw_fallback, observed 2026-08-15T20:16:37.807855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.622958Z digest=sha256:3d60fe364f79341befda9f2c8f9141a2b0485d2c09e751dd783840b1cbf58f1a

Observation ce548559-a3d4-4656-aa8f-3527fdad1767 · outbound

This paper cites What Makes for Good Visual Tokenizers for Large Language Models?.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation What Makes for Good Visual Tokenizers for Large Language Models?

Reference 44

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source=pdf_text observed=2026-08-15T20:16:36.633448Z digest=sha256:6694672044e75b2afced3cf7025efb1b30c592d3bad0f8f8dc0ecf1e71d4d26c

Observation 29281ab3-185d-4244-a02c-96e4c07fcc0d · outbound

This paper cites MaskBit: Embedding-free Image Generation via Bit Tokens.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 45

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source=pdf_text observed=2026-08-15T20:16:36.642689Z digest=sha256:10e2e8731300918fd87da905ca8ebd58112267439a7004639d949f7a4c1b1067

Observation 9cf2c012-4a29-4f47-b383-f2762e1cc16e · outbound

This paper cites Liquid: Language Models are Scalable and Unified Multi-modal Generators.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Liquid: Language Models are Scalable and Unified Multi-modal Generators

Reference 46

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source=pdf_text observed=2026-08-15T20:16:36.649273Z digest=sha256:5cbad25c11209fecdae7de6ce18fae6a3586c2e088bf6b1d19784fd106e340d7

Observation 340e14b2-33d6-4a46-861e-ad764a3fbe8a · outbound

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

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

Reference 47

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source=pdf_text observed=2026-08-15T20:16:36.655133Z digest=sha256:3501181d6b7292cf912887ccc9924686c5cc46e5a663b4b1e5734b4e72503c91

Observation 30cfff84-9fc7-4114-af0a-bfae6c15be7e · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 48

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:16:36.662678Z digest=sha256:a4c119e0e5b56bf2ac9507a3b47bbdc302953e735eafcf7cdfb785213f9df3c2

Observation 76efe441-a51c-424d-bced-dc848b720fc6 · outbound

This paper cites GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation

Reference 49

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source=pdf_text observed=2026-08-15T20:16:36.676564Z digest=sha256:5e6f82e4da0df52b09a086f7f139ebf417c5139b06e3225d08fef0968a51c73c

Observation 8c692157-d8d9-4124-9191-adb6d8deaf03 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Diffusion models: A comprehensive survey of methods and applications

Reference 50

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raw_fallback, observed 2026-08-15T20:16:37.780887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.685791Z digest=sha256:17a1aa9f958aa82cad2f7fcdc6545db4ecf52162f30f68c9dc7e2a8c86bb1ba2

Observation c372fb36-84b4-4a74-b57a-07ccf93ae185 · outbound

This paper cites Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, and Lu Jiang.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, and Lu Jiang

Reference 51

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raw_fallback, observed 2026-08-15T20:16:37.759971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.692217Z digest=sha256:978b4129a4917e0c8c46f096dc35e88064573e9d903a485f751e1df1809777b2

Observation be95616b-2bdc-4a0d-a803-151685542525 · outbound

This paper cites Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A

Reference 52

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raw_fallback, observed 2026-08-15T20:16:37.740883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.697988Z digest=sha256:8135ecb5375cb26fab0a2516835a6e004a332331b0c45ae00821e67c399a76dc

Observation 69feac44-94b4-4641-be7f-0ce8ed4c4eb2 · outbound

This paper cites An image is worth 32 tokens for reconstruction and generation.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation An image is worth 32 tokens for reconstruction and generation

Reference 53

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raw_fallback, observed 2026-08-15T20:16:37.715747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.704093Z digest=sha256:b539fb8aaafdcbd03feb8b268cb9a356aa37f4abfd0d5c3f5f4b2af89510ce4b

Observation 16d4eb7e-1952-43e1-8c7b-b7807d352c6f · outbound

This paper cites A simple LLM framework for long-range video question-answering.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation A simple LLM framework for long-range video question-answering

Reference 54

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raw_fallback, observed 2026-08-15T20:16:37.684490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.711866Z digest=sha256:6b99e33a8fb87011451e2f18532420168d0c847755eeee006695c90864663289

Observation 5c3081dc-376c-4be7-b953-4568a12883b7 · outbound

This paper cites Efros, Eli Shechtman, and Oliver Wang.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Efros, Eli Shechtman, and Oliver Wang

Reference 55

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raw_fallback, observed 2026-08-15T20:16:37.656742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T20:16:36.718820Z digest=sha256:13bba33857faf791bb5e2251c9c46fca2742465537aa52040db71ac4473a3528

Observation 51f42578-6c45-4910-a670-8fdc1494ba87 · outbound

This paper cites Image and Video Tokenization with Binary Spherical Quantization.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Image and Video Tokenization with Binary Spherical Quantization

Reference 56

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source=pdf_text observed=2026-08-15T20:16:36.724968Z digest=sha256:919f1dbcfcdaddc90f69bb158aa90246d61b3a17ec15c69839ca85503348a5ad

Pith citing papers

Observation f64afaca-72dc-4b45-a466-fe99c0a4e46a · inbound

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation cites this paper.

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation

Reference 23

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arxiv_id, observed 2026-05-15T02:13:30.562810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T02:10:18.004724Z digest=sha256:bf8f977a7802fcdbf3898c2304755a5128c3eb426e73284dfb759074d2818120

Observation 21b780be-50b9-42c5-a4de-f83e7427bed3 · inbound

Tokenizer Generator Coupling in Medical Image Generation cites this paper.

Tokenizer Generator Coupling in Medical Image Generation VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation

Reference 6

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source=pdf_text observed=2026-08-11T00:27:00.510598Z digest=sha256:1d1eb0feac15b5b7b882f0ef62652a7c6629d52f949260f4b9bfc1ccc9a7a781