Paper Citation Record · LEDGER

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 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 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.