Paper Citation Record · LEDGER

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 11 inbound Pith citation observations for arXiv:2501.09755.

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

pith.paper-citation-record.v1
2501.09755 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:48:08.265907Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:32:54.714314Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T15:43:53.781661Z

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy8
unresolved27
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External citation measurements

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Outbound references

Observation b35607cf-99de-48b5-8b3b-0741941ed404 · outbound

This paper cites an unresolved cited work.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:48:08.654487Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a76d1005-9940-4863-8258-c894aad87378 · outbound

This paper cites Unified Auto-Encoding with Masked Diffusion.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Unified Auto-Encoding with Masked Diffusion

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.144364Z

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Observation 12bdf26c-6073-4028-ae31-c78688c6010b · outbound

This paper cites Autoregressive Image Generation using Residual Quantization.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Autoregressive Image Generation using Residual Quantization

Reference 10

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Observation 3dc2b18a-3d6d-4a22-b309-6cf0235b761f · outbound

This paper cites Decoupled Weight Decay Regularization.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Decoupled Weight Decay Regularization

Reference 11

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Observation ba747b7c-4db7-4479-ab14-cbe8c7d9875d · outbound

This paper cites Finite Scalar Quantization: VQ-VAE Made Simple.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Finite Scalar Quantization: VQ-VAE Made Simple

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.173627Z

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Observation 7918047c-6bbd-4215-9854-a9c167ced876 · outbound

This paper cites A tokenizer designed for efficient processing of large-scale datasets.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation A tokenizer designed for efficient processing of large-scale datasets

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.731010Z

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Observation 20f6135c-b6dd-4ffb-bb08-13f31de8116d · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 15

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Observation be8d39b6-1658-4929-9f09-f306e7ae52f5 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Movie Gen: A Cast of Media Foundation Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.189670Z

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Observation d7f9c7a1-135f-458a-8c35-6e17b82a5169 · outbound

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

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.198018Z

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Observation 8b115cdb-cedb-4424-86fc-6f46695f1ae4 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.202298Z

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Observation dc066984-6293-4d49-b410-98d8ce1f3cd8 · outbound

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

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.206529Z

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Observation 64bf2786-e14d-45ad-a00e-e252a9d2fbff · outbound

This paper cites Video Occupancy Models.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Video Occupancy Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.210707Z

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Observation 07b1dca9-a14f-4664-8329-b74f503ec422 · outbound

This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.214934Z

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Observation d32b1449-baa1-49cb-9d7c-a05999221b60 · outbound

This paper cites ElasticTok: Adaptive Tokenization for Image and Video.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation ElasticTok: Adaptive Tokenization for Image and Video

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.226775Z

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Observation 6737044d-731d-4629-8795-d114f4c6a526 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.234771Z

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Observation f4a97e9a-7635-442f-95a5-32fa1a303fa6 · outbound

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

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.238826Z

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Observation 85ac8289-37b4-47be-b2f2-2893a1459a15 · outbound

This paper cites Apollo: An Exploration of Video Understanding in Large Multimodal Models.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Apollo: An Exploration of Video Understanding in Large Multimodal Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.242746Z

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Observation ac779b7f-16db-41e3-809e-1d29cf7680aa · outbound

This paper cites an unresolved cited work.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:48:08.706258Z

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Observation 4ec8c220-c5f8-432d-b2a6-3b331f242b95 · outbound

This paper cites The architecture consists of Transformer blocks (Vaswani et al.,.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation The architecture consists of Transformer blocks (Vaswani et al.,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.692782Z

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Observation f7d49cc9-be21-4894-a707-b58201a7a151 · outbound

This paper cites Additionally, we integrate video processing code from Apollo (Zohar et al.,.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Additionally, we integrate video processing code from Apollo (Zohar et al.,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.680802Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c64ea08-317e-4f7c-ba52-704646a3792c · outbound

This paper cites Overall, ViTok leverages advanced training techniques and architectural innovations to achieve state-of-the-art performance in image and video reconstruction and generation tasks.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Overall, ViTok leverages advanced training techniques and architectural innovations to achieve state-of-the-art performance in image and video reconstruction and generation tasks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.668859Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d8f0a97d-cdf9-450a-863a-f6cf45e8874b · outbound

This paper cites Consequently, it provides an alternative to Simple ViTok with greater control over the latent space.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Consequently, it provides an alternative to Simple ViTok with greater control over the latent space

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.641138Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68eb0807-21bd-480f-b0eb-372a09a15e8c · outbound

This paper cites Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas

Reference 2004

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Observation 33d2fd40-5049-4518-a1e4-e766afda89cb · outbound

This paper cites LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior

Reference 2008

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Observation 6997ee64-89eb-4561-a6a3-3cacb09b1727 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Classifier-Free Diffusion Guidance

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.153068Z

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Observation e7abd60d-e829-46b7-a0c5-e97216cee5b6 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.139919Z

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Observation 57359005-bf6a-4c54-9d7d-5ad3254b298b · outbound

This paper cites GLU Variants Improve Transformer.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation GLU Variants Improve Transformer

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.193450Z

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Observation ca83b6d8-6896-4a8f-a7c8-625e7de5a3cf · outbound

This paper cites Improving neural networks by preventing co-adaptation of feature detectors.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Improving neural networks by preventing co-adaptation of feature detectors

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.148759Z

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Observation 82e18de8-d31e-4e50-a8a1-57ac643aa548 · outbound

This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.161562Z

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Observation 90d7f7c3-a8f4-4ddc-a334-57124dd7e326 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Perceptual losses for real-time style transfer and super-resolution

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.742934Z

Source-reported events for the cited work

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Observation 49525f17-50ad-417c-a538-189bf0fcf3f4 · outbound

This paper cites Layer Normalization.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Layer Normalization

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.126056Z

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Observation ec66f98c-c9cb-4194-be2a-452ff883e8e8 · outbound

This paper cites Video generation models as world simulators.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Video generation models as world simulators

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.754031Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0bf26328-b53f-418d-967c-769f8a5515ca · outbound

This paper cites Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:48:08.719302Z

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Observation b843b63e-f2d6-4f91-82ec-6f8eff33911f · outbound

This paper cites Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer

Reference 2024

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Observation b55997f5-f1f5-4024-82d7-7b2d4262b30b · outbound

This paper cites Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 2025

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Pith citing papers

Observation 09dadb60-9087-4483-9174-0060d7155eec · inbound

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization cites this paper.

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:32:54.714314Z

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Observation a76ca4bf-c885-4701-ab4f-dcb157ef389b · inbound

Flow marching for a generative PDE foundation model cites this paper.

Flow marching for a generative PDE foundation model Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:51:25.457933Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2aa396cf-8da4-4bbc-b54a-8e497bd7139a · inbound

TC-AE: Unlocking Token Capacity for Deep Compression Autoencoders cites this paper.

TC-AE: Unlocking Token Capacity for Deep Compression Autoencoders Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:15:57.138473Z

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Observation ac0c1624-78af-40ce-9719-35b212adf4b2 · inbound

Latent-Compressed Variational Autoencoder for Video Diffusion Models cites this paper.

Latent-Compressed Variational Autoencoder for Video Diffusion Models Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:03.077326Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0ace6f3-d26d-4699-b281-320374b491fa · inbound

ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters cites this paper.

ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:51:06.445578Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d0055b1f-9d88-459b-bc31-c64cb3afe703 · inbound

LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation cites this paper.

LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:56:30.898606Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e29255c-bf86-4f97-9053-82a549e05319 · inbound

What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion cites this paper.

What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:05:57.333354Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3dbd517c-af80-47d7-b9be-5335072a6892 · inbound

Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation cites this paper.

Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:46:29.699689Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0bbab4b-173f-4e72-9dca-99de3929f7dc · inbound

Vision Foundation Models as Generalist Tokenizers for Image Generation cites this paper.

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

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 51798922-a3bf-4a4f-9b45-9dd38f20c13a · inbound

Balancing Image Compression and Generation with Bootstrapped Tokenization cites this paper.

Balancing Image Compression and Generation with Bootstrapped Tokenization Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:55.456464Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c02e9aa8-1cbf-473b-a748-2a9cb2180d83 · inbound

Multiplayer Interactive World Models with Representation Autoencoders cites this paper.

Multiplayer Interactive World Models with Representation Autoencoders Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Reference 101

Resolution: metadata mismatch; local_arxiv, observed 2026-07-07T15:43:53.783116Z

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