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Paper Citation Record · LEDGER

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model

As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.16776.

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

pith.paper-citation-record.v1
2506.16776 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:42:35.252934Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

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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

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation c56500b3-b7b2-4a8c-98a6-579eb691440e · outbound

This paper cites Generative adversarial networks,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Generative adversarial networks,

Reference 2

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Observation 67537da1-24dc-4935-aa2d-22cdac5a5813 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Diffusion models beat gans on image synthesis,

Reference 3

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Observation 189bf7c6-253c-4e4e-a7fc-99909d13f4b3 · outbound

This paper cites Auto-Encoding Variational Bayes.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Auto-Encoding Variational Bayes

Reference 4

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Observation bc350214-d451-4700-9ff2-eacbda798e08 · outbound

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

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Score-Based Generative Modeling through Stochastic Differential Equations

Reference 5

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Observation c2867b64-3504-4403-a544-c206b79ebc1b · outbound

This paper cites Repaint: Inpainting using denoising diffu- sion probabilistic models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Repaint: Inpainting using denoising diffu- sion probabilistic models,

Reference 6

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Observation 7daf66fc-bb85-4ef1-9031-13aeeed282d1 · outbound

This paper cites Permutation invariant graph generation via score- based generative modeling,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Permutation invariant graph generation via score- based generative modeling,

Reference 7

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Source-reported events for the cited work

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Observation 15c7324a-6289-47f2-be73-5048b08449db · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 8

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Unavailable: canonical work link unavailable.

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Observation ec4ff8de-7ea9-4941-821f-6af74bcfd45c · outbound

This paper cites Post-training quantization on diffusion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Post-training quantization on diffusion models,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation db0c871a-4fc3-4f61-8032-656db439ac85 · outbound

This paper cites Q-diffusion: Quantizing diffusion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Q-diffusion: Quantizing diffusion models,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bd336daa-7a00-48bb-ba3d-5583e7769951 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Progressive Distillation for Fast Sampling of Diffusion Models

Reference 11

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Observation e3d2ddbb-1bfe-4962-a484-340daa8455ae · outbound

This paper cites On distillation of guided diffusion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model On distillation of guided diffusion models,

Reference 12

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Source-reported events for the cited work

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Observation e084e7b3-a7ab-467d-a8f6-78dec4fdf43b · outbound

This paper cites Denoising diffusion prob- abilistic models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Denoising diffusion prob- abilistic models,

Reference 13

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Source-reported events for the cited work

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Observation 8951d92a-f79d-45eb-81c3-956180fd4b15 · outbound

This paper cites Denoising Diffusion Implicit Models.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Denoising Diffusion Implicit Models

Reference 14

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Observation 9d77e289-48a4-4d74-8de8-f58db35b086f · outbound

This paper cites Improved denoising diffusion probabilistic models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Improved denoising diffusion probabilistic models,

Reference 16

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Source-reported events for the cited work

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Observation cb6fca8a-85c8-4bc8-9758-47fc51917af0 · outbound

This paper cites Structural pruning for diffusion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Structural pruning for diffusion models,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 90baa611-439b-4212-86db-bdbc8c50298e · outbound

This paper cites Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0b61cb41-8a3e-4ca2-93fb-174268c7a3d8 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Distilling the Knowledge in a Neural Network

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0ab8105c-b9cd-468e-ab9c-4bb50330cfbb · outbound

This paper cites Towards effective low-bitwidth convolutional neural networks,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Towards effective low-bitwidth convolutional neural networks,

Reference 20

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Source-reported events for the cited work

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Observation 93b4b2bc-404c-43ad-9313-e965a8521deb · outbound

This paper cites Effective training of convolutional neural networks with low-bitwidth weights and activations,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Effective training of convolutional neural networks with low-bitwidth weights and activations,

Reference 21

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Source-reported events for the cited work

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Observation 6aaa9dd3-f00d-469e-9260-62412d9e00f7 · outbound

This paper cites Adaptive loss-aware quantization for multi-bit networks,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Adaptive loss-aware quantization for multi-bit networks,

Reference 22

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Source-reported events for the cited work

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Observation 73050cd5-5454-4974-ac5e-51efc5f0af48 · outbound

This paper cites CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps

Reference 23

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Source-reported events for the cited work

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Observation 951f6e5b-1a4d-4147-828e-1a2c6d42ceb9 · outbound

This paper cites Learning to quantize deep networks by optimizing quantization intervals with task loss,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Learning to quantize deep networks by optimizing quantization intervals with task loss,

Reference 24

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Observation aeb45565-028c-4aa7-8192-40fb9488a248 · outbound

This paper cites Bit-shrinking: Limiting instantaneous sharpness for improving post-training quantization,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Bit-shrinking: Limiting instantaneous sharpness for improving post-training quantization,

Reference 25

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Observation d75d662a-83d2-4660-89cf-a43a6ab86fd5 · outbound

This paper cites Vari- ational diffusion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Vari- ational diffusion models,

Reference 26

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Source-reported events for the cited work

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Observation 4fe087d4-2610-40b1-a476-86b6eb9d3975 · outbound

This paper cites Temporal dynamic quantization for diffusion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Temporal dynamic quantization for diffusion models,

Reference 27

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3b1a4ae5-e97e-42a7-99a2-bd15e532743e · outbound

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

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Tfmq-dm: Temporal feature maintenance quantization for diffusion models,

Reference 28

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Source-reported events for the cited work

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Observation c99d7221-b0a9-4730-9854-8fc5cb56b368 · outbound

This paper cites Up or down? adaptive rounding for post- training quantization,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Up or down? adaptive rounding for post- training quantization,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 57abf048-e823-4285-9eb7-ce4255798b0c · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0b7f45a3-2ad7-45a5-a526-e72f39c4ba3e · outbound

This paper cites High-resolution image synthesis with latent diffu- sion models,.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model High-resolution image synthesis with latent diffu- sion models,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f1688ff2-f41e-42d8-bca7-1aa5554578cc · outbound

This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 32

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Unavailable: canonical work link unavailable.

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Observation dccd5cfe-54e6-427a-9325-b1addcea781f · outbound

This paper cites Learned Step Size Quantization.

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model Learned Step Size Quantization

Reference 33

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Unavailable: canonical work link unavailable.

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