Paper Citation Record · LEDGER

Progressive Compression with Universally Quantized Diffusion Models

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2412.10935.

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

pith.paper-citation-record.v1
2412.10935 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:35:10.238883Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T14:12:31.253468Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:07:36.761108Z

Reference resolution

14 of 14 outbound references displayed

verified exact3
verified fuzzy8
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b0057f8-2c17-4adf-84d4-171ee6c750ea · outbound

This paper cites posterior.

Progressive Compression with Universally Quantized Diffusion Models posterior

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.493524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c012c4a5-9624-4901-b3f1-8df1bc3a34a8 · outbound

This paper cites Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters.

Progressive Compression with Universally Quantized Diffusion Models Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:35:10.399707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2c2fdf41-8b83-4215-9f30-cbac34b2daa7 · outbound

This paper cites High-Fidelity Image Compression with Score-based Generative Models.

Progressive Compression with Universally Quantized Diffusion Models High-Fidelity Image Compression with Score-based Generative Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T15:35:10.197036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbbfab55-3ef3-4c72-a0f9-4fcbfb324d18 · outbound

This paper cites probability-flow.

Progressive Compression with Universally Quantized Diffusion Models probability-flow

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.455086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 278b2049-5851-48a3-a8e3-08e808558bf5 · outbound

This paper cites Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation.

Progressive Compression with Universally Quantized Diffusion Models Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T15:35:10.205316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68b5f09e-8545-4ae3-8a58-a5b626978d72 · outbound

This paper cites Lossy Compression with Gaussian Diffusion.

Progressive Compression with Universally Quantized Diffusion Models Lossy Compression with Gaussian Diffusion

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T15:35:10.209751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5fec48d-a830-4539-af32-6ad31e43e1bc · outbound

This paper cites 15 It therefore suffices to show that ωt converges in distribution to N (0, β2 T |tI) in the continuous-time limit.

Progressive Compression with Universally Quantized Diffusion Models 15 It therefore suffices to show that ωt converges in distribution to N (0, β2 T |tI) in the continuous-time limit

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.481723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 20d1be49-4761-4988-af0b-05da77700a21 · outbound

This paper cites We conclude by the Lindeberg-Feller theorem that Ωt = Xn,1 +.

Progressive Compression with Universally Quantized Diffusion Models We conclude by the Lindeberg-Feller theorem that Ωt = Xn,1 +

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.467710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98587f73-d02f-4c7c-94e2-86ed317b8856 · outbound

This paper cites Right: Ablation of the influence of model size on validation loss.

Progressive Compression with Universally Quantized Diffusion Models Right: Ablation of the influence of model size on validation loss

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.442839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a21c2e7-0147-41df-a56d-75358c938912 · outbound

This paper cites When compressing a single 32x32 CIFAR image, we observe file size overhead ≤ 3% of the theoretical NELBO.

Progressive Compression with Universally Quantized Diffusion Models When compressing a single 32x32 CIFAR image, we observe file size overhead ≤ 3% of the theoretical NELBO

Reference 128

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.428938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8458e239-8a65-4377-a49c-14731662ec95 · outbound

This paper cites The forward process is defined by q(zt|x) := N (αtx, σ2 t I), where αt and σ2 t are positive scalar-valued functions of t.

Progressive Compression with Universally Quantized Diffusion Models The forward process is defined by q(zt|x) := N (αtx, σ2 t I), where αt and σ2 t are positive scalar-valued functions of t

Reference 1992

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.505287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0e9377e1-e1a8-426c-917c-09a68e2df839 · outbound

This paper cites Communication requirements for generating correlated random variables.

Progressive Compression with Universally Quantized Diffusion Models Communication requirements for generating correlated random variables

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:35:10.517284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b9f374d-1760-4209-97af-ff08abd3dba7 · outbound

This paper cites DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding.

Progressive Compression with Universally Quantized Diffusion Models DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding

Reference 2022

Resolution: verified exact; raw_fallback, observed 2026-08-11T15:35:10.375301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 898579ad-5316-4eb0-80f7-d3f7f24a5d1f · outbound

This paper cites On Channel Simulation with Causal Rejection Samplers.

Progressive Compression with Universally Quantized Diffusion Models On Channel Simulation with Causal Rejection Samplers

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:35:10.415752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation e96728f1-094f-4f68-9865-59463bf73ca8 · inbound

Few-step Generative Models as Lossy Compression cites this paper.

Few-step Generative Models as Lossy Compression Progressive Compression with Universally Quantized Diffusion Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:07:36.762763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.