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Paper Citation Record · LEDGER

Federated Reinforcement Learning to Optimize Teleoperated Driving Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.02312.

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

pith.paper-citation-record.v1
2410.02312 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:02:40.340938Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T23:52:41.031268Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d94d99ee-21e3-4082-a7fa-442352ad739a · inbound

Statistical Analysis and End-to-End Performance Evaluation of Traffic Models for Automotive Data cites this paper.

Statistical Analysis and End-to-End Performance Evaluation of Traffic Models for Automotive Data Federated Reinforcement Learning to Optimize Teleoperated Driving Networks

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-16T12:02:40.340938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:02:40.340938Z digest=sha256:c23ccb93cf1faad3ff3dc795a50e05d105cc0463869b915b09676d099befb102

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

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving cites this paper.

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.

source=pdf_text observed=2026-08-15T23:52:40.883180Z digest=sha256:078ede4b1990ff83eb4c510e620f17ae0a0560910f544313414e7b9dfac288fb