Paper Citation Record · LEDGER

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.05960.

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

pith.paper-citation-record.v1
2506.05960 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:22:02.536888Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy28
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b93e05f-90a3-4d5e-8137-b34b444d4375 · outbound

This paper cites Ballard, Joshua Bambrick, Se- bastian W.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Ballard, Joshua Bambrick, Se- bastian W

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:03.050147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb766756-18d5-4ad1-98e3-f126b4396e51 · outbound

This paper cites fvcore: Collection of common code that’s shared among different research projects in fair computer vision team.https://github.com/facebookresearch/ fvcore, 2019.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models fvcore: Collection of common code that’s shared among different research projects in fair computer vision team.https://github.com/facebookresearch/ fvcore, 2019

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:03.033373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 391a3f78-ba1d-4fb1-8f65-b6e1ab8c5563 · outbound

This paper cites Li, and Li Fei-Fei.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Li, and Li Fei-Fei

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:03.012754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c95a36e7-f5e6-4665-8d8d-dbef4386689b · outbound

This paper cites Diffu- sion models beat gans on image synthesis.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Diffu- sion models beat gans on image synthesis

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.996352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cfb637f9-20c9-4720-a33a-2da3ddcebbd3 · outbound

This paper cites Extreme com- pression of large language models via additive quantization.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Extreme com- pression of large language models via additive quantization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.980186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 50297342-d959-4da9-9401-0b55a18bd3b2 · outbound

This paper cites Scaling rec- tified flow transformers for high-resolution image synthesis.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Scaling rec- tified flow transformers for high-resolution image synthesis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.965914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1456f7f4-baad-44ef-acb4-911b926e04a6 · outbound

This paper cites EfficientDM: Efficient quantization-aware fine- tuning of low-bit diffusion models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models EfficientDM: Efficient quantization-aware fine- tuning of low-bit diffusion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.950635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6ed2d442-e424-4e99-8952-d81700aed23e · outbound

This paper cites Ptqd: Accurate post-training quantization for diffusion models.Advances in Neural Information Pro- cessing Systems, 36, 2024.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Ptqd: Accurate post-training quantization for diffusion models.Advances in Neural Information Pro- cessing Systems, 36, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.935911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d2c42431-1abe-4cc3-a5c4-eefe58284f62 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.921318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b68fab20-8229-49cd-8c1d-38d21217b3de · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:02.441488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c56d0e3-11be-44d8-add9-9a6bfd122859 · outbound

This paper cites Tfmq-dm: Temporal feature maintenance quantization for diffusion models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Tfmq-dm: Temporal feature maintenance quantization for diffusion models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.895259Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b004ff2-3c41-43be-a3b2-078ea0e335f9 · outbound

This paper cites Mistral 7B.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Mistral 7B

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:02.449943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a743ead8-d558-44fd-a78e-d7b377e0d239 · outbound

This paper cites Kingma and Jimmy Ba.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Kingma and Jimmy Ba

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.880821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9f46e68f-78ba-4cdc-9b63-b7da2b42eaf7 · outbound

This paper cites Q-diffusion: Quantizing diffusion models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Q-diffusion: Quantizing diffusion models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.865784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8bd7d87e-ef46-4f10-b0ae-d4aad9b36db0 · outbound

This paper cites Q-dm: An efficient low-bit quantized dif- fusion model.Advances in Neural Information Processing Systems, 36, 2024.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Q-dm: An efficient low-bit quantized dif- fusion model.Advances in Neural Information Processing Systems, 36, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.850920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 03b3aea5-6a92-4f77-bae3-e5dc54bacda6 · outbound

This paper cites Pseudo numerical methods for diffusion models on manifolds.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Pseudo numerical methods for diffusion models on manifolds

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.836058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2feffa07-8c9b-4cfd-a976-8e779a4266b1 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787,.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787,

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b67867c-044a-4a2e-ac0a-25910da76067 · outbound

This paper cites Pv-tuning: Beyond straight-through es- timation for extreme LLM compression.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Pv-tuning: Beyond straight-through es- timation for extreme LLM compression

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.811384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db1a9f74-6a85-4150-8d80-b0ed75371ad5 · outbound

This paper cites On distillation of guided diffusion models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models On distillation of guided diffusion models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.797111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8671141b-6c0c-431f-8072-ab4dda573829 · outbound

This paper cites Yang, Zachary DeVito, Mar- tin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Yang, Zachary DeVito, Mar- tin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.783463Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 075bd2eb-cb7e-47b3-bd9c-309e2d476e3e · outbound

This paper cites Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.769537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e87b5ef-bbc6-4116-b3ab-530c04ba49e5 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models U-net: Convolutional networks for biomedical image segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.755210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 96ffdd30-fe90-4d46-a79d-3f8c83f2c29c · outbound

This paper cites an unresolved cited work.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:22:02.740488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e8f75bf-1a41-45f7-9fe0-f03cb01cdfb1 · outbound

This paper cites Progressive distillation for fast sampling of diffusion models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Progressive distillation for fast sampling of diffusion models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.725699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f27e5bb0-6c83-43bb-b26a-499b6894c30d · outbound

This paper cites Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.711396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bea3fb2f-af73-4ac3-90f7-4c99f83876e3 · outbound

This paper cites Post-training quantization on diffusion models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Post-training quantization on diffusion models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.697131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dc368ac3-2242-4222-b7fa-f50ae3b8db87 · outbound

This paper cites Temporal dynamic quantization for dif- fusion models.Advances in Neural Information Processing Systems, 36, 2024.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Temporal dynamic quantization for dif- fusion models.Advances in Neural Information Processing Systems, 36, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.682542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4582537-e9bb-4b2e-96a6-8f1b4be7db60 · outbound

This paper cites Denois- ing diffusion implicit models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Denois- ing diffusion implicit models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.666252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2e06d942-45bd-4c7e-af9f-59569bdb450c · outbound

This paper cites Bitsfusion: 1.99 bits weight quantization of diffusion model.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Bitsfusion: 1.99 bits weight quantization of diffusion model

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.651830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 825f1cfb-5c26-4cc9-9213-0052f340ef69 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:22:02.524169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 458f2b1d-2376-4dec-8a39-3b76e0c1726c · outbound

This paper cites Quip#: Even better LLM quantization with hadamard incoherence and lattice code- books.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Quip#: Even better LLM quantization with hadamard incoherence and lattice code- books

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.636872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 945ee67f-b9aa-4fd3-8723-36ce88bd48b2 · outbound

This paper cites QTIP: quantization with trellises and incoherence pro- cessing.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models QTIP: quantization with trellises and incoherence pro- cessing

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.621522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee5a14ad-20eb-4116-8f79-38283e8874b9 · outbound

This paper cites Quest: Low-bit diffusion model quantization via efficient selective finetuning, 2024.

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models Quest: Low-bit diffusion model quantization via efficient selective finetuning, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:22:02.606483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.