Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:52:40.956366Z
Paper Citation Record · LEDGER
Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving
As of 21 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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.