Paper Citation Record · LEDGER

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving

As of 19 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2505.03558.

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

pith.paper-citation-record.v1
2505.03558 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:52:40.956366Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy20
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03a9ba62-9a33-4b65-badf-c71274912ddc · outbound

This paper cites Toward 6G Networks: Use Cases and Technologies,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Toward 6G Networks: Use Cases and Technologies,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.318465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aadacd2e-5003-49a6-b104-bd9692ccbe7a · outbound

This paper cites Toward automated vehicle teleoperation: Vision, opportuni- ties, and challenges,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Toward automated vehicle teleoperation: Vision, opportuni- ties, and challenges,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.305480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff7ba2af-f351-4fac-ad23-c83a99fd166f · outbound

This paper cites C-V2X Use Cases V olume II: Examples and Service Level Requirements,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving C-V2X Use Cases V olume II: Examples and Service Level Requirements,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.292402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8f9013ce-3f78-4865-9004-228a86216306 · outbound

This paper cites Millimeter-wave vehicular communication to support massive automotive sensing,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Millimeter-wave vehicular communication to support massive automotive sensing,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.279528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a8b31dd-c1f8-4e17-8c07-48e2955699b4 · outbound

This paper cites Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.266144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98f9e5a4-dfac-47fb-bc2b-8e2717c5c9a0 · outbound

This paper cites A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.251912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a145cba-89fd-4ace-85a4-317b349199b2 · outbound

This paper cites Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.239136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7303710f-7dd7-474b-a920-c99f22d3ef71 · outbound

This paper cites Federated Reinforcement Learning to Optimize Teleoperated Driving Networks.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Federated Reinforcement Learning to Optimize Teleoperated Driving Networks

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:52:41.037427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39c8e2f9-7a4a-4213-a0a2-f5dfc4f46e53 · outbound

This paper cites Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.226073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bc9d5833-b90b-4bc0-a2d5-b305f5b87e6e · outbound

This paper cites Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.213126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b79c23d-e589-4e59-9c64-4995fd3a1fcb · outbound

This paper cites 5G Resource Scheduling for Low-latency Communication: A Reinforcement Learning Approach,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving 5G Resource Scheduling for Low-latency Communication: A Reinforcement Learning Approach,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.200414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2c5d8ee-2999-45c3-9cca-9d9bc3c741e6 · outbound

This paper cites End-to-End Simulation of 5G mmWave Networks,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving End-to-End Simulation of 5G mmWave Networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.186930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93662350-26b1-4c23-a3de-daffb743cfd3 · outbound

This paper cites Recent devel- opment and applications of SUMO - Simulation of Urban MObility,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Recent devel- opment and applications of SUMO - Simulation of Urban MObility,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.172637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29c86f4a-9836-4f36-9ba0-ae9a4798b0e0 · outbound

This paper cites Geometry-based vehicle-to- vehicle channel modeling for large-scale simulation,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Geometry-based vehicle-to- vehicle channel modeling for large-scale simulation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.158569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a879c3e6-fde5-451a-be7a-2516eecdd64b · outbound

This paper cites (2017) Draco 3D Data Compression.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving (2017) Draco 3D Data Compression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.145405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2051afe6-f7eb-4501-8d70-a2a926cc288b · outbound

This paper cites Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.132162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 849b4d1d-202a-4993-8c67-131f4055e2b2 · outbound

This paper cites NR and NG-RAN Overall Description (Release 15),.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving NR and NG-RAN Overall Description (Release 15),

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.118400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3462fe83-4fad-447e-b311-bb9a43cf24d3 · outbound

This paper cites an unresolved cited work.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:52:40.926273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 614a190e-6126-4c63-b8dd-04954ff7540e · outbound

This paper cites The complexity of decentralized control of Markov decision processes,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving The complexity of decentralized control of Markov decision processes,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.095444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 68303114-24cc-4d58-9387-782c138d5401 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Proximal Policy Optimization Algorithms

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:52:40.934992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edc5311e-e860-4fc2-b82c-200eac8e7418 · outbound

This paper cites Trust region policy optimization,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Trust region policy optimization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.080012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bc2eaf5f-d9ef-4c62-8063-e3b6cadfc8ee · outbound

This paper cites High- dimensional continuous control using generalized advantage estimation,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving High- dimensional continuous control using generalized advantage estimation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.065742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6d4f5ca-8456-4045-832b-f37e39fa839a · outbound

This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:52:40.947511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0cec324-f5cb-479a-948f-0c2fb723dfe1 · outbound

This paper cites The surprising effectiveness of PPO in cooperative multi-agent games,.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving The surprising effectiveness of PPO in cooperative multi-agent games,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:52:41.051740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a79c9291-51df-4bc2-8aea-f6f4abf8281d · outbound

This paper cites An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning.

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T23:52:40.956366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.