Paper Citation Record · LEDGER

Pruning for Sparse Diffusion Models based on Gradient Flow

As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2501.09464.

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

pith.paper-citation-record.v1
2501.09464 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:04:29.619116Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:11:59.590780Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy16
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36e8a790-3758-466f-8a07-8f831271335b · outbound

This paper cites ”Denoising Diffusion Prob- abilistic Models.” Advances in Neural Information Processing Systems (NeurIPS), 2020.

Pruning for Sparse Diffusion Models based on Gradient Flow ”Denoising Diffusion Prob- abilistic Models.” Advances in Neural Information Processing Systems (NeurIPS), 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.270586Z

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.

Observation a67cfef4-f267-4cec-a49c-2f6ae6b2da84 · outbound

This paper cites Improved Denoising Diffusion Probabilistic Models.

Pruning for Sparse Diffusion Models based on Gradient Flow Improved Denoising Diffusion Probabilistic Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.457700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e8fb3a-7e9c-4cf5-9a71-1a5008559ff5 · outbound

This paper cites Kingma, Abhishek Ku- mar, Stefano Ermon, and Ben Poole.

Pruning for Sparse Diffusion Models based on Gradient Flow Kingma, Abhishek Ku- mar, Stefano Ermon, and Ben Poole

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.254947Z

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.

Observation 73e28709-0531-4666-97d1-8931a10e3d32 · outbound

This paper cites Higher angular momentum pairings in interorbital shadowed-triplet superconductors: Application to Sr$_{2}$RuO$_{4}$.

Pruning for Sparse Diffusion Models based on Gradient Flow Higher angular momentum pairings in interorbital shadowed-triplet superconductors: Application to Sr$_{2}$RuO$_{4}$

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:04:29.971209Z

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.

Observation 40588ea9-6bf1-4f3c-bfe0-bd459ca72cd9 · outbound

This paper cites The structure of the unit group of the group algebra $F(C_3 \times D_{10})$.

Pruning for Sparse Diffusion Models based on Gradient Flow The structure of the unit group of the group algebra $F(C_3 \times D_{10})$

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:04:29.946822Z

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.

Observation 32454064-77e6-47a7-94fa-905c8d00119d · outbound

This paper cites Weiss, Mohammad Norouzi, and William Chan.

Pruning for Sparse Diffusion Models based on Gradient Flow Weiss, Mohammad Norouzi, and William Chan

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.238033Z

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.

Observation d4fc3c28-2b3d-4965-811f-41b136dbd79d · outbound

This paper cites ”DiffWave: A Versatile Diffusion Model for Audio Synthesis.” International Conference on Learning Representations (ICLR), 2021.

Pruning for Sparse Diffusion Models based on Gradient Flow ”DiffWave: A Versatile Diffusion Model for Audio Synthesis.” International Conference on Learning Representations (ICLR), 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.221364Z

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.

Observation c79dd648-c3ae-4623-be5c-fb63609e4052 · outbound

This paper cites Diffusion-LM Improves Controllable Text Generation.

Pruning for Sparse Diffusion Models based on Gradient Flow Diffusion-LM Improves Controllable Text Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.490949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cbeded0-87f7-4d86-9c61-a473b7cf8a58 · outbound

This paper cites A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity.

Pruning for Sparse Diffusion Models based on Gradient Flow A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.496036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 974c78b6-2983-4e5f-b318-68a54e85cd5b · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Pruning for Sparse Diffusion Models based on Gradient Flow Diffusion Models Beat GANs on Image Synthesis

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.501547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1da17136-a7b9-407d-aaf6-a4f51a87b154 · outbound

This paper cites ”Fast Sampling of Diffusion Models with Exponential Integrator.” Advances in Neural Information Processing Systems (NeurIPS), 2021.

Pruning for Sparse Diffusion Models based on Gradient Flow ”Fast Sampling of Diffusion Models with Exponential Integrator.” Advances in Neural Information Processing Systems (NeurIPS), 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.205955Z

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.

Observation 4aed746c-46e5-49ff-8c14-cc2b403661eb · outbound

This paper cites Simulated assessment of light transport through ischaemic skin flaps.

Pruning for Sparse Diffusion Models based on Gradient Flow Simulated assessment of light transport through ischaemic skin flaps

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:04:29.868440Z

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.

Observation 46180976-4d6a-4838-906e-69ac0023ea52 · outbound

This paper cites ”Kdgan: Knowledge distillation with generative ad- versarial networks.” Advances in neural information processing systems 31 (2018).

Pruning for Sparse Diffusion Models based on Gradient Flow ”Kdgan: Knowledge distillation with generative ad- versarial networks.” Advances in neural information processing systems 31 (2018)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.189556Z

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.

Observation 525765ae-2c98-4282-9b66-ac274a555619 · outbound

This paper cites SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification.

Pruning for Sparse Diffusion Models based on Gradient Flow SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T20:04:29.845994Z

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.

Observation aa8410c7-640f-4033-8c94-3a91967bbe69 · outbound

This paper cites Denoising Diffusion Implicit Models.

Pruning for Sparse Diffusion Models based on Gradient Flow Denoising Diffusion Implicit Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.525759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cab3ba3-4e76-4581-97f8-51265906be3d · outbound

This paper cites ”Structural Pruning for Diffusion Models.” Advances in Neural Information Processing Systems (NeurIPS), 2023.

Pruning for Sparse Diffusion Models based on Gradient Flow ”Structural Pruning for Diffusion Models.” Advances in Neural Information Processing Systems (NeurIPS), 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.175240Z

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.

Observation c79327e5-685e-4985-9dd4-072aa6d5f5eb · outbound

This paper cites SparseDM: Toward Sparse Efficient Diffusion Models.

Pruning for Sparse Diffusion Models based on Gradient Flow SparseDM: Toward Sparse Efficient Diffusion Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.535162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee3f3b9e-5188-41ce-8a90-d973fa3088f1 · outbound

This paper cites Optimal transport: old and new.

Pruning for Sparse Diffusion Models based on Gradient Flow Optimal transport: old and new

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.159846Z

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.

Observation 64b813fb-67eb-4667-bd4d-bb8338ee31a5 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

Pruning for Sparse Diffusion Models based on Gradient Flow Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.544583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da6a49e0-51c2-4da0-b351-7fca9d507fcb · outbound

This paper cites ”Soft masking for cost-constrained channel prun- ing.” European Conference on Computer Vision.

Pruning for Sparse Diffusion Models based on Gradient Flow ”Soft masking for cost-constrained channel prun- ing.” European Conference on Computer Vision

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.144145Z

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.

Observation de055d5a-2ba9-4c9a-870a-e49c214a8032 · outbound

This paper cites Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask.

Pruning for Sparse Diffusion Models based on Gradient Flow Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.554318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14c752e4-ec20-4568-845a-31b473c18f58 · outbound

This paper cites Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch.

Pruning for Sparse Diffusion Models based on Gradient Flow Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.559032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40ddcdab-37fe-4c51-a869-c7f4b44989a4 · outbound

This paper cites A Gradient Flow Framework For Analyzing Network Pruning.

Pruning for Sparse Diffusion Models based on Gradient Flow A Gradient Flow Framework For Analyzing Network Pruning

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:04:29.740579Z

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.

Observation b9cb37fb-701c-4122-8716-607945bcdc6a · outbound

This paper cites T., Wan, B., Zhang, H., Chen, J.,.

Pruning for Sparse Diffusion Models based on Gradient Flow T., Wan, B., Zhang, H., Chen, J.,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.127153Z

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.

Observation f2e0a7e7-eed0-4189-8c98-e4d05da81032 · outbound

This paper cites T., Wan, B., Zhang, H., Chen, J., Wang, J., & Li, B.

Pruning for Sparse Diffusion Models based on Gradient Flow T., Wan, B., Zhang, H., Chen, J., Wang, J., & Li, B

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.110650Z

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.

Observation 97b03de5-bdc1-4006-a76a-ceeb4850bf22 · outbound

This paper cites an unresolved cited work.

Pruning for Sparse Diffusion Models based on Gradient Flow Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:04:30.093930Z

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.

Observation 51a85230-abc3-409b-b32b-917796b5d198 · outbound

This paper cites ”Only train once: A one-shot neural network training and pruning framework.” Advances in Neural Information Processing Systems 34 (2021): 19637-19651.

Pruning for Sparse Diffusion Models based on Gradient Flow ”Only train once: A one-shot neural network training and pruning framework.” Advances in Neural Information Processing Systems 34 (2021): 19637-19651

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.075783Z

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.

Observation 9525d510-523a-43ed-9bdf-40913eb08ae2 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Pruning for Sparse Diffusion Models based on Gradient Flow The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.584828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a6dc40b-7005-4caa-a337-14745c777bcb · outbound

This paper cites ”Learning multiple layers of features from tiny images.” (2009): 7.

Pruning for Sparse Diffusion Models based on Gradient Flow ”Learning multiple layers of features from tiny images.” (2009): 7

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.061268Z

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.

Observation 186716bc-f45e-4414-a516-2137dc070a41 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Pruning for Sparse Diffusion Models based on Gradient Flow Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.593405Z

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Unavailable: canonical work link unavailable.

Observation 6b66810d-062f-449a-953a-e7e4337a3cc4 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Pruning for Sparse Diffusion Models based on Gradient Flow LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.598226Z

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Unavailable: canonical work link unavailable.

Observation 54374657-5357-4301-b19d-b8afcbb3b9c3 · outbound

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

Pruning for Sparse Diffusion Models based on Gradient Flow Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.044570Z

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.

Observation 8ad5731a-40fe-4c5d-a847-740d20171a94 · outbound

This paper cites Bovik, Hamid R.

Pruning for Sparse Diffusion Models based on Gradient Flow Bovik, Hamid R

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.028497Z

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.

Observation a8f622a9-0517-42d6-be86-c8c0944c3a7c · outbound

This paper cites Channel pruning for accelerat- ing very deep neural networks.

Pruning for Sparse Diffusion Models based on Gradient Flow Channel pruning for accelerat- ing very deep neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:04:30.008770Z

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.

Observation c28bc86c-fe93-4ddd-a98c-a421f0e92cc0 · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Pruning for Sparse Diffusion Models based on Gradient Flow Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.619116Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation e2cbe16c-8873-48a8-aad6-302e03ab9c91 · inbound

DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration cites this paper.

DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration Pruning for Sparse Diffusion Models based on Gradient Flow

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T19:11:59.590780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.