Paper Citation Record · LEDGER

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks

As of 11 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2412.18212.

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

pith.paper-citation-record.v1
2412.18212 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:59:29.605882Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:35:39.001692Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:35:49.737474Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy30
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff369787-9587-43b3-a230-77eb2966c4f6 · outbound

This paper cites A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.417825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6153531-2c5e-49f4-9919-81948a1d04ef · outbound

This paper cites Next-word prediction: A perspective of energy-aware distributed inference,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Next-word prediction: A perspective of energy-aware distributed inference,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.213896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2157f247-c3c8-4f99-bf4b-308644f0907c · outbound

This paper cites Training language models to follow instructions with human feedback,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Training language models to follow instructions with human feedback,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.426866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56d770f9-eb69-40b5-b78b-f6757b5428a5 · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:30.193782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d9b92fb0-7233-4a97-98c4-bd984f9be8ee · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.435263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c3f510d-4c4e-46c8-9484-0a3f8689b712 · outbound

This paper cites Blockchain-based efficient and trustworthy aigc services in metaverse,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Blockchain-based efficient and trustworthy aigc services in metaverse,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.181833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4c1fc963-fdc6-4663-ae3b-9539be63533a · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:30.170060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a3fb9f49-1ec1-445d-b4de-1614542540e5 · outbound

This paper cites Incentive mechanisms for online task offloading with privacy-preserving in uav-assisted mobile edge computing,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Incentive mechanisms for online task offloading with privacy-preserving in uav-assisted mobile edge computing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.154516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5ded7272-819c-4ea7-94b3-225bad01f3f0 · outbound

This paper cites Asteroid: Resource- efficient hybrid pipeline parallelism for collaborative dnn training on heterogeneous edge devices,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Asteroid: Resource- efficient hybrid pipeline parallelism for collaborative dnn training on heterogeneous edge devices,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.142233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 967620a1-ab42-4836-b8d1-a55d10c8cbe7 · outbound

This paper cites Invar: Inversion aware resource provisioning and workload scheduling for edge computing,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Invar: Inversion aware resource provisioning and workload scheduling for edge computing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.128955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 694ce7a2-b822-4acc-bfcb-d19bea0d04db · outbound

This paper cites Online optimization of dnn inference network utility in collaborative edge computing,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Online optimization of dnn inference network utility in collaborative edge computing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.116098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cd64156d-7832-47bc-9233-76b81c60770a · outbound

This paper cites Edgetuner: Fast scheduling algorithm tuning for dynamic edge-cloud workloads and resources,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Edgetuner: Fast scheduling algorithm tuning for dynamic edge-cloud workloads and resources,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.103021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b60ff41f-8c1a-4554-b2fa-2de338d6bd08 · outbound

This paper cites Edge computing and sensor-cloud: Overview, solutions, and directions,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Edge computing and sensor-cloud: Overview, solutions, and directions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.090579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 270bf016-0fd4-4ff6-8303-f422a4a2ea81 · outbound

This paper cites Dynamic parallel multi-server selection and allocation in collaborative edge computing,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Dynamic parallel multi-server selection and allocation in collaborative edge computing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.078295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3cd5ed55-2851-4869-ba1c-dd46b124d012 · outbound

This paper cites Collaborative service placement, task scheduling, and resource allocation for task offloading with edge-cloud cooperation,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Collaborative service placement, task scheduling, and resource allocation for task offloading with edge-cloud cooperation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.064916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f5c1abc3-03c3-4a13-95de-c61abbf21dd6 · outbound

This paper cites An online joint optimization approach for qoe maximization in uav-enabled mobile edge computing,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks An online joint optimization approach for qoe maximization in uav-enabled mobile edge computing,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.050922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6fc4022-cfc9-4256-99bd-816fba2f8b76 · outbound

This paper cites Improved algorithms for co-scheduling of edge analytics and routes for uav fleet missions,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Improved algorithms for co-scheduling of edge analytics and routes for uav fleet missions,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:30.038645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cafe4c44-2627-4f11-ba19-afe1c027625d · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:30.025652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3dbaa613-6440-437e-909b-b2623fe4e3de · outbound

This paper cites Denoising diffusion probabilistic models,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Denoising diffusion probabilistic models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.495309Z

Source-reported events for the cited work

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Observation 9a543132-a0ab-441f-8451-2475c95086f3 · outbound

This paper cites Ai- generated incentive mechanism and full-duplex semantic communica- tions for information sharing,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Ai- generated incentive mechanism and full-duplex semantic communica- tions for information sharing,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.995063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 693d592f-0e41-499e-9afa-6dfb98791d8a · outbound

This paper cites Chatgpt: five priorities for research,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Chatgpt: five priorities for research,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.981131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 76e4238a-0cf3-4d19-b5b7-840e664d7da2 · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:29.968055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7d1faeb-f912-4081-a763-b3715e8c37bb · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.511660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afb29dcd-0369-4942-897c-83db6ccd42aa · outbound

This paper cites Enhancing ai-generated content efficiency through adaptive multi- edge collaboration,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Enhancing ai-generated content efficiency through adaptive multi- edge collaboration,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.955482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5b27d2bb-794c-4c19-a8e7-01560f6d950d · outbound

This paper cites Diffusion-based reinforcement learning for edge-enabled ai-generated content services,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Diffusion-based reinforcement learning for edge-enabled ai-generated content services,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.943438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 93544735-b482-4431-b3a2-058431a2f327 · outbound

This paper cites Sparks of generative pretrained transformers in edge intelligence for the metaverse: Caching and inference for mobile artificial intelligence- generated content services,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Sparks of generative pretrained transformers in edge intelligence for the metaverse: Caching and inference for mobile artificial intelligence- generated content services,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.929407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc62a625-c6e4-4e11-b309-39692f83481b · outbound

This paper cites Human-level control through deep reinforcement learning,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Human-level control through deep reinforcement learning,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.527891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ac3aeae-651e-4a27-bdcb-0a81a5e7d2d9 · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.908538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a1cb11a8-1e74-4f0e-8b77-3349689e54e7 · outbound

This paper cites Multi-user layer- aware online container migration in edge-assisted vehicular networks,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Multi-user layer- aware online container migration in edge-assisted vehicular networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.896042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6e28fb3b-80fd-4f43-89f2-7aa67ce0af14 · outbound

This paper cites Deep reinforcement learning for task offloading in mobile edge computing systems,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Deep reinforcement learning for task offloading in mobile edge computing systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.883091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b7198333-52ac-46d9-ae5b-6da4c98e2072 · outbound

This paper cites Applications of deep reinforcement learning in communications and networking: A survey,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Applications of deep reinforcement learning in communications and networking: A survey,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.870167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3b8de8e7-5fea-4256-af8b-593453bac78b · outbound

This paper cites Diffusion models in vision: A survey,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Diffusion models in vision: A survey,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.548154Z

Source-reported events for the cited work

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Observation c7deb7b1-2516-45d7-9799-08957b209922 · outbound

This paper cites Exploring collaborative distributed diffusion-based ai- generated content (aigc) in wireless networks,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Exploring collaborative distributed diffusion-based ai- generated content (aigc) in wireless networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.847856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f7c0c59-3b7e-4433-b267-46eb8131e1fe · outbound

This paper cites Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.555751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7b2423f-477c-42b2-a524-63d3ceeb903c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks LLaMA: Open and Efficient Foundation Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T04:59:29.560366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecf4bf62-2688-4759-af2c-13990459af02 · outbound

This paper cites When deep rein- forcement learning meets federated learning: intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks When deep rein- forcement learning meets federated learning: intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.826954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba4690d0-7424-4d3f-84f4-05134cf1fa07 · outbound

This paper cites Service placement and request scheduling for data-intensive applications in edge clouds,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Service placement and request scheduling for data-intensive applications in edge clouds,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.814650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a1a2783c-a3f7-467c-9f89-d61396253378 · outbound

This paper cites Layer aware microservice placement and request scheduling at the edge,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Layer aware microservice placement and request scheduling at the edge,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.801541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 75c1edbe-63a7-44e0-943d-aff0f69ed2af · outbound

This paper cites Online optimal service selection, resource allocation and task offloading for multi-access edge computing: A utility-based approach,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Online optimal service selection, resource allocation and task offloading for multi-access edge computing: A utility-based approach,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.787802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3985a88d-059b-43e3-9119-0eda3ed0e5c3 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis,.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Scaling rectified flow transformers for high-resolution image synthesis,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.774482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7304f41d-50d1-44ac-948b-4262db4bd326 · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:29.755280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4e33865-0b73-4fa3-8cc4-ed81a9dcf1c7 · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:29.742149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 86574177-747a-4995-a472-9b916e8205a5 · outbound

This paper cites 13 Changfu Xu (Student Member, IEEE) received the B.S.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks 13 Changfu Xu (Student Member, IEEE) received the B.S

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.729142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3d6f469d-940b-4f92-9117-cfb3f414d79c · outbound

This paper cites His research interests include Internet of Things, edge computing, and mobile computing.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks His research interests include Internet of Things, edge computing, and mobile computing

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.703307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c57d8ee0-519f-407b-ba81-1c3558349b6f · outbound

This paper cites He is a member of IEEE/ACM/CCF.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks He is a member of IEEE/ACM/CCF

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:59:29.716377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 62c07ef7-c8e4-4936-acaf-99edf529d306 · outbound

This paper cites an unresolved cited work.

Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:59:29.689905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 9fe724e1-340c-4212-aec2-9a5279a13283 · inbound

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences cites this paper.

Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:35:49.818772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.