Paper Citation Record · LEDGER

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2608.05346.

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

pith.paper-citation-record.v1
2608.05346 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:55:44.166713Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy12
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57053204-a27f-4240-b346-a17bfa5ca8be · outbound

This paper cites IEEE Standard for local and metropolitan area networks—bridges and bridged networks—amendment 25: Enhancements for scheduled traffic,IEEE Standard 802.1qbv-2015, 2016, pp. 1–57,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application IEEE Standard for local and metropolitan area networks—bridges and bridged networks—amendment 25: Enhancements for scheduled traffic,IEEE Standard 802.1qbv-2015, 2016, pp. 1–57,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.332010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0eb5a907-d373-4f8c-ab1b-5332e823d1a1 · outbound

This paper cites Performance analysis of the integra- tion of dynamic cloud computing environments and tsn networks,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Performance analysis of the integra- tion of dynamic cloud computing environments and tsn networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.322058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b60c8e3e-27ef-40d7-b94e-110eaa6da038 · outbound

This paper cites A survey of schedul- ing algorithms for the time-aware shaper in time-sensitive networking (tsn),.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application A survey of schedul- ing algorithms for the time-aware shaper in time-sensitive networking (tsn),

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.312254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80a45ce0-9596-42f8-83a0-8c8847be8587 · outbound

This paper cites Time- sensitive networking (tsn) for industrial automation: Current advances and future directions,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Time- sensitive networking (tsn) for industrial automation: Current advances and future directions,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T14:55:44.126279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96a4b31d-0d29-410d-82aa-85aab096d62a · outbound

This paper cites Reinforce- ment learning based routing for time-aware shaper scheduling in time- sensitive networks,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Reinforce- ment learning based routing for time-aware shaper scheduling in time- sensitive networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.296903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 815aac74-ee9e-4a5f-ad8e-cbf6ee7b9fe1 · outbound

This paper cites Deepscheduler: En- abling flow-aware scheduling in time-sensitive networking,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Deepscheduler: En- abling flow-aware scheduling in time-sensitive networking,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.286109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 96de13d7-4d49-4822-b629-5f381c55b06c · outbound

This paper cites Ai-based dynamic schedule calculation in time sensitive networks using gcn-td3,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Ai-based dynamic schedule calculation in time sensitive networks using gcn-td3,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.275316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 113a21a9-e6b6-4390-93c1-3768354ed98d · outbound

This paper cites Deterministic scheduling for asymmetric flows in future wireless networks,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Deterministic scheduling for asymmetric flows in future wireless networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.263711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2cba989a-937f-45e1-849f-3462329e8ccd · outbound

This paper cites Configuring the ieee 802.1 q time-aware shaper with deep reinforcement learning,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Configuring the ieee 802.1 q time-aware shaper with deep reinforcement learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.251762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 90c7b170-a180-4498-8adf-c53e44723bdd · outbound

This paper cites Mitigation of scheduling violations in time- sensitive networking using deep deterministic policy gradient,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Mitigation of scheduling violations in time- sensitive networking using deep deterministic policy gradient,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.241755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 85c25f2b-3f7e-4ab7-bcc7-7ed2468caa35 · outbound

This paper cites Convergence of reinforcement learning and time-sensitive networking for future industrial ai agent communication: Fundamentals, challenges, and opportunities,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Convergence of reinforcement learning and time-sensitive networking for future industrial ai agent communication: Fundamentals, challenges, and opportunities,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.233106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2218156e-1c60-4456-9cef-55a246be9aff · outbound

This paper cites Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T14:55:44.155987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11fbb440-3834-4c89-bfcb-3ab2a5cb1ccb · outbound

This paper cites An extended reality offloading ip traffic dataset and models,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application An extended reality offloading ip traffic dataset and models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.224407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05b957dc-ad1e-48c6-b0cc-44fe82988b5f · outbound

This paper cites From pixels to packets: Traffic classification of augmented reality and cloud gaming,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application From pixels to packets: Traffic classification of augmented reality and cloud gaming,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:55:44.214761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa4fd8cf-9285-47ba-8cca-5f9fd690fa8d · outbound

This paper cites Asynchronous methods for deep rein- forcement learning,.

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application Asynchronous methods for deep rein- forcement learning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T14:55:44.166713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.