Paper Citation Record · LEDGER

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2506.08850.

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

pith.paper-citation-record.v1
2506.08850 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:05:05.316215Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

22 of 22 outbound references displayed

verified exact1
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5707701-416c-40a6-9c21-62f01e4d5159 · outbound

This paper cites A Robust Scheduling Algorithm for Overload- Tolerant Real-Time Systems.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing A Robust Scheduling Algorithm for Overload- Tolerant Real-Time Systems

Reference 1

Resolution: verified exact; raw_fallback, observed 2026-08-07T05:05:05.653969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79dacc2b-0698-4081-b786-5969f143184c · outbound

This paper cites Soft real-time scheduling.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Soft real-time scheduling

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:06.006037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d0aabb2-ed54-4b33-b5ce-769aa82f715b · outbound

This paper cites Scheduling IoT applications in edge and fog computing environments: a taxonomy and future directions.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Scheduling IoT applications in edge and fog computing environments: a taxonomy and future directions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.985277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a072b38-2182-461b-8d6a-7d1ca0becda2 · outbound

This paper cites TF-DDRL: ATransformer-enhanced DistributedDRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing TF-DDRL: ATransformer-enhanced DistributedDRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.966018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81eb67d5-6f6b-4077-a483-ae5951a450fd · outbound

This paper cites Resource scheduling in edge computing: A survey.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Resource scheduling in edge computing: A survey

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.948561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a56dae03-5a9c-4f37-9366-205615cbaad5 · outbound

This paper cites A comprehensive survey on reinforcement- learning-based computation offloading techniques in edge computing systems.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing A comprehensive survey on reinforcement- learning-based computation offloading techniques in edge computing systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.929832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6082c933-e4fc-423b-9ca0-f331bbcc6ce8 · outbound

This paper cites A state-of-the-art review of task scheduling for edge computing: A delay-sensitive application perspective.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing A state-of-the-art review of task scheduling for edge computing: A delay-sensitive application perspective

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.907690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ab1958f-47b8-42f8-8bc1-dabc64c3fc0a · outbound

This paper cites Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Reference 8

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:05:05.549945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 35e7602c-b9b9-481f-8fce-2c95546e51c8 · outbound

This paper cites Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.890281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84112773-51aa-4199-abe3-c13d1dad9a3d · outbound

This paper cites Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.873173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 838e7d2a-06b4-4104-83dc-bfb869569872 · outbound

This paper cites Deep Reinforcement Learn- ing Based Distributed Computation Offloading in Vehicular Edge Computing Networks.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Deep Reinforcement Learn- ing Based Distributed Computation Offloading in Vehicular Edge Computing Networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.854602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed9c096a-7ffe-4f51-9b88-5e171489d0db · outbound

This paper cites GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.833777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbb055df-6aeb-4820-a389-c87d023ba69b · outbound

This paper cites Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehic- ular Edge Computing.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehic- ular Edge Computing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.812760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5961fa45-8d13-434d-a5c0-247e7b05123a · outbound

This paper cites Deep reinforcement learning based ap- proach for online service placement and computation resource allocation in edge computing.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Deep reinforcement learning based ap- proach for online service placement and computation resource allocation in edge computing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.797462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d90f73b9-ff8d-44a6-a335-a6bda1fb6ca3 · outbound

This paper cites Urbanenqosplace: A deep reinforcement learning model for service placement of real-time smart city iot applications.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Urbanenqosplace: A deep reinforcement learning model for service placement of real-time smart city iot applications

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.779786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4abffa64-ecdc-4697-946e-1d502248cc93 · outbound

This paper cites Deep reinforcement learning-based online re- source management for uav-assisted edge computing with dual connectivity.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Deep reinforcement learning-based online re- source management for uav-assisted edge computing with dual connectivity

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.762709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f3d69b3-c4f0-44ef-9b26-bfea7abcd3c5 · outbound

This paper cites MESON: A mobility-aware dependent task offloading scheme for urban vehicular edge com- puting.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing MESON: A mobility-aware dependent task offloading scheme for urban vehicular edge com- puting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.747095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb331640-e086-468e-afee-f117e760c3e3 · outbound

This paper cites Deep reinforcement learning-based task assign- ment for cooperative mobile edge computing.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Deep reinforcement learning-based task assign- ment for cooperative mobile edge computing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.730632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8337427b-63b2-48f4-8a76-2bde3a2a19bb · outbound

This paper cites Smart Resource Allocation for Mobile Edge Comput- ing: A Deep Reinforcement Learning Approach.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Smart Resource Allocation for Mobile Edge Comput- ing: A Deep Reinforcement Learning Approach

Reference 19

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:05:05.441573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad21bbca-5b66-4a0c-8f5b-616f6eae1f50 · outbound

This paper cites A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.713305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2fbcc4a4-f3fc-4987-b694-7d546712adce · outbound

This paper cites EdgeSimPy: Python-Based Modeling and Sim- ulation of Edge Computing Resource Management Policies.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing EdgeSimPy: Python-Based Modeling and Sim- ulation of Edge Computing Resource Management Policies

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.691409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca0339f4-62e4-4385-9de3-f5ccd2e04851 · outbound

This paper cites Convergent policy optimization for safe reinforce- ment learning.

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing Convergent policy optimization for safe reinforce- ment learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:05.671390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.