Paper Citation Record · LEDGER

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks

As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2608.05340.

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

pith.paper-citation-record.v1
2608.05340 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:03:59.487746Z

measured 20 of 20 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 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

20 of 20 outbound references displayed

verified exact0
verified fuzzy12
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4c644aa-6445-48ac-bdb5-624df265f6a6 · outbound

This paper cites Edge learning via federated split decision transformers for metaverse resource allocation,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Edge learning via federated split decision transformers for metaverse resource allocation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.673153Z

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 3a50d94d-ae75-48fd-8bf8-998ec914dfe4 · outbound

This paper cites Self-play ensemble q-learning enabled resource allocation for network slicing,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Self-play ensemble q-learning enabled resource allocation for network slicing,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.663381Z

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 4fce180c-0680-446e-8b79-d84522b33428 · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Performance analysis of the integra- tion of dynamic cloud computing environments and tsn networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.438753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dde7e31-9e2a-444c-9432-d3e8b698eea7 · 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 Transformer for Queue-Level XR Traffic Scheduling in TSN Networks 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 4

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.441497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549a26ff-c0cd-4ad2-b1f9-99596bc29ea6 · outbound

This paper cites Enhancing mobile immersive streaming experience via deadline-aware scheduling and learning-enhanced congestion control,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Enhancing mobile immersive streaming experience via deadline-aware scheduling and learning-enhanced congestion control,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.644202Z

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 021a67ab-bf52-490b-b622-ba7b0d83ac70 · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks A survey of schedul- ing algorithms for the time-aware shaper in time-sensitive networking (tsn),

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.447373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ca1db81-7219-4984-bf9d-c028978da431 · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Configuring the ieee 802.1 q time-aware shaper with deep reinforcement learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.450260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9ad734f-c7bd-4ff0-9d34-e0edb6578b0d · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Mitigation of scheduling violations in time- sensitive networking using deep deterministic policy gradient,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.452771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 407ae727-bd0e-446d-9845-544e0ad5bd91 · outbound

This paper cites Cooperative resource allocation and traffic scheduling for iiot controllers in edge clouds: A hierarchical reinforcement learning approach,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Cooperative resource allocation and traffic scheduling for iiot controllers in edge clouds: A hierarchical reinforcement learning approach,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.618917Z

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 1d106265-0c86-45dd-9a26-85919bce9673 · outbound

This paper cites Towards distributed flow scheduling in ieee 802.1 qbv time-sensitive networks,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Towards distributed flow scheduling in ieee 802.1 qbv time-sensitive networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.609755Z

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 b567b72c-23f5-4476-89b5-5e1c267f6f1d · outbound

This paper cites Multi-agent reinforcement learning-based routing and scheduling models in time-sensitive networking for internet of vehicles communi- cations between transportation field cabinets,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Multi-agent reinforcement learning-based routing and scheduling models in time-sensitive networking for internet of vehicles communi- cations between transportation field cabinets,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.600393Z

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 6a8aaa2a-418e-4b1a-801c-7babab3a70cb · outbound

This paper cites Sharp: A study on safe heterogeneous agent reinforcement learning paradigm for 5g-tsn traffic scheduling,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Sharp: A study on safe heterogeneous agent reinforcement learning paradigm for 5g-tsn traffic scheduling,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.590494Z

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 5f2d1e32-8e19-4652-b8b6-6c0384fbfee2 · outbound

This paper cites Multi-agent reinforcement learning is a sequence modeling problem,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Multi-agent reinforcement learning is a sequence modeling problem,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.581099Z

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 b19e441b-f2b8-4850-8b49-e43fd1d6340e · outbound

This paper cites Semantic communi- cations in networked systems: A data significance perspective,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Semantic communi- cations in networked systems: A data significance perspective,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.570697Z

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 ea55cb7d-5f7c-400a-841b-e60542d5a3d2 · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks An extended reality offloading ip traffic dataset and models,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.472439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc9e060f-c566-4d25-ade2-65abea0f09ab · outbound

This paper cites Methodology and infrastructure for tsn-based reproducible network experiments,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Methodology and infrastructure for tsn-based reproducible network experiments,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.552264Z

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 4158fd78-7d1c-43d9-9f3d-9857b425fecf · outbound

This paper cites Pdu-set scheduling algorithm for xr traffic in multi-service 5g-advanced networks,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Pdu-set scheduling algorithm for xr traffic in multi-service 5g-advanced networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.541636Z

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 f7bcd007-7604-4193-9441-96e9f37bdc8a · outbound

This paper cites Multi-agent transformer approach for collaborative task offloading and resource optimization in noma-based vehicular edge computing,.

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Multi-agent transformer approach for collaborative task offloading and resource optimization in noma-based vehicular edge computing,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:03:59.531182Z

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 42c4b036-5321-495e-9912-a3a022640b46 · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Asynchronous methods for deep rein- forcement learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.484833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 748a4a37-ff95-438e-836e-6ee1bd1fd78b · outbound

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

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T15:03:59.487746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.