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Paper Citation Record · LEDGER

Diffusion Models on the Edge: Challenges, Optimizations, and Applications

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 3 inbound Pith citation observations for arXiv:2504.15298.

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

pith.paper-citation-record.v1
2504.15298 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:34:24.376661Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T00:49:42.135227Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T22:19:00.469051Z

Reference resolution

27 of 27 outbound references displayed

  • verified exact0
  • verified fuzzy19
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 82bd8d21-9f3b-482c-9b12-d35f27635ef6 · outbound

This paper cites Denoising diffusion probabilistic models,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Denoising diffusion probabilistic models,

Reference 1

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no resolver link, observed 2026-08-16T12:34:24.249499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e67f2cd0-c476-453d-9a9a-9fa8ea66849d · outbound

This paper cites Score-based generative modeling through stochastic differential equations,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Score-based generative modeling through stochastic differential equations,

Reference 2

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raw_fallback, observed 2026-08-16T12:34:24.826688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation bfa897bc-d048-4248-91e7-286904ce0a91 · outbound

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

Diffusion Models on the Edge: Challenges, Optimizations, and Applications High- resolution image synthesis with latent diffusion models,

Reference 3

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no resolver link, observed 2026-08-16T12:34:24.259775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 748626dc-9692-4c51-b00b-24f090307937 · outbound

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

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Diffusion models beat GANs on image synthesis,

Reference 4

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raw_fallback, observed 2026-08-16T12:34:24.800328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation bd881251-616b-45f4-91c6-77c91595f625 · outbound

This paper cites Theoretical evaluation of oxynitride, oxyfluoride and nitrofluoride perovskites with promising photon absorption properties for solar water splitting.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Theoretical evaluation of oxynitride, oxyfluoride and nitrofluoride perovskites with promising photon absorption properties for solar water splitting

Reference 5

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metadata mismatch
local_arxiv, observed 2026-08-16T12:34:24.520714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation c61cd54e-d65b-49e0-b8e6-c0fde74f9798 · outbound

This paper cites Arm Ethos-U55 NPU,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Arm Ethos-U55 NPU,

Reference 6

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raw_fallback, observed 2026-08-16T12:34:24.784735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 367fcc0b-0e18-4842-984b-ee6f51e6f985 · outbound

This paper cites Benchmarking TinyML Systems: Challenges and Direction,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Benchmarking TinyML Systems: Challenges and Direction,

Reference 7

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raw_fallback, observed 2026-08-16T12:34:24.769485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation b1b169a8-fe15-42ba-81c6-1b6688006dbe · outbound

This paper cites Edge AI: On-demand accelerating deep learning inference on edge devices,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Edge AI: On-demand accelerating deep learning inference on edge devices,

Reference 8

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raw_fallback, observed 2026-08-16T12:34:24.754388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 76663ab6-cb27-4087-a771-d729fe8c57cf · outbound

This paper cites CMSIS-NN: Efficient neural network kernels for Arm Cortex-M CPUs,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications CMSIS-NN: Efficient neural network kernels for Arm Cortex-M CPUs,

Reference 9

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raw_fallback, observed 2026-08-16T12:34:24.738496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.290574Z digest=sha256:0811e637960dd9c6a6d82e21768c5788bc30e7d259074293703a28d432b900ba

Observation e33a6f50-d303-4e97-9307-fd134a3f9426 · outbound

This paper cites Homological Dimensions of Extriangulated Categories and Recollements.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Homological Dimensions of Extriangulated Categories and Recollements

Reference 10

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metadata mismatch
local_arxiv, observed 2026-08-16T12:34:24.497701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 66fd4bd2-b2e9-48e0-ad4b-5a74efbf8eef · outbound

This paper cites Improved Denoising Diffusion Proba- bilistic Models,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Improved Denoising Diffusion Proba- bilistic Models,

Reference 11

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raw_fallback, observed 2026-08-16T12:34:24.723643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation aed2159a-30e0-443c-94f0-949e08a3707a · outbound

This paper cites DPM-Solver: A Fast ODE Solver for Diffusion Proba- bilistic Models,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications DPM-Solver: A Fast ODE Solver for Diffusion Proba- bilistic Models,

Reference 12

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raw_fallback, observed 2026-08-16T12:34:24.708009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9eed70ed-1f5e-4986-91e0-f3e280812077 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 13

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raw_fallback, observed 2026-08-16T12:34:24.692867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.310296Z digest=sha256:57403e786fecee7c3cef8a35789db742ff30df1c6f9bd680e587b26459cac1aa

Observation c29339ce-60bc-4837-9cf1-9a8fe88aed20 · outbound

This paper cites Distilling the knowledge in a neural network,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Distilling the knowledge in a neural network,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-16T12:34:24.676741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.315450Z digest=sha256:8b0282fbbcaaefddafcd4e5c02f3cc589f90f82e297640b888747a37e70fbca6

Observation c803c9cb-b669-46d9-9ad7-1b262260a69c · outbound

This paper cites RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:34:24.320252Z digest=sha256:cf97be2aafcf470db1788323236495d779f4fda47fed2b7a23f5b65e49bdf880

Observation 1f661c31-2395-414f-96ab-e89baad92480 · outbound

This paper cites TVM: An Automated End-to-End Optimiz- ing Compiler for Deep Learning,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications TVM: An Automated End-to-End Optimiz- ing Compiler for Deep Learning,

Reference 16

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raw_fallback, observed 2026-08-16T12:34:24.661546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 79dd6942-6dfc-411c-8512-ee0aa7a6d62f · outbound

This paper cites Accelerating diffusion models via hardware-aware neural architecture search,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Accelerating diffusion models via hardware-aware neural architecture search,

Reference 17

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raw_fallback, observed 2026-08-16T12:34:24.646287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 87549289-6d73-4ffa-8919-51e8b1ee285d · outbound

This paper cites Charge Stripe Manipulation of Superconducting Pairing Symmetry Transition.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Charge Stripe Manipulation of Superconducting Pairing Symmetry Transition

Reference 18

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metadata mismatch
local_arxiv, observed 2026-08-16T12:34:24.459061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 34949739-79ce-4f7b-bad2-b15dfc429ffb · outbound

This paper cites Edge AI: On-Demand Accelerated Inference via Adaptive Deep Compression,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Edge AI: On-Demand Accelerated Inference via Adaptive Deep Compression,

Reference 19

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raw_fallback, observed 2026-08-16T12:34:24.630998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.339589Z digest=sha256:24520a242a2cdf22660f9eacae0ca4b9d1221b87ed69d431b991142d1ab47fce

Observation 193d4ad1-ad1e-446f-8a8a-5e230deeada9 · outbound

This paper cites Deep Learning on LoRa Edge Devices: Benchmarking and Analysis,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Deep Learning on LoRa Edge Devices: Benchmarking and Analysis,

Reference 20

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raw_fallback, observed 2026-08-16T12:34:24.615548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.344222Z digest=sha256:d2c70c13df756b97c6055d00774ae54f8c8bfe7341582421ee3560b3bed6acb8

Observation aa823e1c-f62c-4f52-80df-d1e944b08681 · outbound

This paper cites Warden and D.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Warden and D

Reference 21

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.349103Z digest=sha256:33773d654422bf818dab1a9515ad56e05d9969f0fa8fc46e3936c9a4c0d68fba

Observation 19bc93c6-dc80-47cc-bee2-cebb300c948b · outbound

This paper cites MCUNet: Tiny Deep Learning on IoT Devices,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications MCUNet: Tiny Deep Learning on IoT Devices,

Reference 22

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raw_fallback, observed 2026-08-16T12:34:24.582743Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5355399b-59d7-41b0-bf7f-1b5fca43fefc · outbound

This paper cites Once-for-all: Train one network and specialize it for efficient deployment,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Once-for-all: Train one network and specialize it for efficient deployment,

Reference 23

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raw_fallback, observed 2026-08-16T12:34:24.566950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.357919Z digest=sha256:6007769eba5e458a7585369d8e0cc296525fc4da8f22715bc0495803c2dd6d1b

Observation 41ec60b9-1b51-4bb1-bf85-0b00064ad306 · outbound

This paper cites Accelerating ML on Arm Cortex-M with Ethos-U55,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Accelerating ML on Arm Cortex-M with Ethos-U55,

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-16T12:34:24.551776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.362494Z digest=sha256:2dac29676f4957d43504fd9e8fb54cdb0fcc34b4832dddaf326af0d5729ca689

Observation 54161b3b-ac8a-4a7b-b305-bcee3961f783 · outbound

This paper cites Kendryte K210 Datasheet and Developer Guide,.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Kendryte K210 Datasheet and Developer Guide,

Reference 25

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raw_fallback, observed 2026-08-16T12:34:24.536115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T12:34:24.366999Z digest=sha256:c5bfd7a46bb6a248278d136a9f75fa32ff0d7d89723e2f4450e3ffe4a1f4670d

Observation 39384d5b-a683-4983-ae0b-61f27a353157 · outbound

This paper cites Horizon physics of quasi-one-dimensional tilted Weyl cones on a lattice.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Horizon physics of quasi-one-dimensional tilted Weyl cones on a lattice

Reference 26

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no resolver link, observed 2026-08-16T12:34:24.371596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:34:24.371596Z digest=sha256:7caa3a58a88254223589e9a083da86165e644f46a7a748e032128e4d0a4caeb1

Observation 2e0776c7-e906-49f6-93f5-8e76b36225e0 · outbound

This paper cites Adding Conditional Control to Text-to-Image Diffusion Models.

Diffusion Models on the Edge: Challenges, Optimizations, and Applications Adding Conditional Control to Text-to-Image Diffusion Models

Reference 27

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unresolved
no resolver link, observed 2026-08-16T12:34:24.376661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:34:24.376661Z digest=sha256:64ad84b0320b8b093c27aba3b71eab48b72d1d2ebf96eaf5688be81a01abfc96

Pith citing papers

Observation 4de453d4-fe12-41fb-99b3-24e6c4aec40f · inbound

Training-Free Generative Sampling via Moment-Matched Score Smoothing cites this paper.

Training-Free Generative Sampling via Moment-Matched Score Smoothing Diffusion Models on the Edge: Challenges, Optimizations, and Applications

Reference 27

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verified exact
arxiv_id, observed 2026-05-15T02:33:32.454343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-15T02:31:15.134624Z digest=sha256:3cc93d23e0105f13ce3b586e956872ac7913d6dd3af64f60b64516b1d9578b5d

Observation ab5e98cd-49f7-4e48-9746-5429eb158673 · inbound

RISE: Relay Inference and Online Scheduling for Efficient Edge-Device Collaborative Diffusion Model Services cites this paper.

RISE: Relay Inference and Online Scheduling for Efficient Edge-Device Collaborative Diffusion Model Services Diffusion Models on the Edge: Challenges, Optimizations, and Applications

Reference 11

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verified exact
arxiv_id, observed 2026-07-03T22:19:00.470440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T23:38:08.990338Z digest=sha256:b655c88d5323aaa967d0ae4b17ff100c8ba5bfa3cce6be9fd554dd95dc2ede01

Observation 0a9f3e4e-65cc-4746-9e00-ba29f3f1c586 · inbound

CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation cites this paper.

CODA: Algorithm-Hardware Co-design for Edge Video Diffusion via NMP-Enabled Compute-Cache Operator Disaggregation Diffusion Models on the Edge: Challenges, Optimizations, and Applications

Reference 75

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unresolved
no resolver link, observed 2026-08-02T00:49:42.135227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:49:42.135227Z digest=sha256:d49c6f5eb057a18a64086b157ff9beec6201bfa3413f32537ef02c39a4d25bc1