Paper Citation Record · LEDGER

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2506.02657.

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

pith.paper-citation-record.v1
2506.02657 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:23:22.252500Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

11 of 11 outbound references displayed

verified exact0
verified fuzzy8
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d703e64-2195-4495-91b9-b006d30206fa · outbound

This paper cites an unresolved cited work.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:23:21.137344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2235412c-3371-403b-b0c4-c4f3d0a6172d · outbound

This paper cites Reinforcement- learning-enabled massive internet of things for 6g wireless communi- cations,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Reinforcement- learning-enabled massive internet of things for 6g wireless communi- cations,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:23.742880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a234e6b3-0aa9-4f0e-a43d-71dafadc3f21 · outbound

This paper cites Q-learning based reinforcement learning approach for lane keeping,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Q-learning based reinforcement learning approach for lane keeping,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:23.547411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37cc86c2-34db-4251-bdf2-f380a59b17e6 · outbound

This paper cites A reinforcement learning-based adaptive path tracking approach for autonomous driving,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning A reinforcement learning-based adaptive path tracking approach for autonomous driving,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:23.393216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 882a8f16-e411-4fc6-8e1e-fc37d880f368 · outbound

This paper cites Digital twin networks: A survey,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Digital twin networks: A survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:23.219649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a3128b0-eed3-45ea-9e16-c14da99e05d1 · outbound

This paper cites Digital twin in industry: State-of-the-art,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Digital twin in industry: State-of-the-art,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:23:21.679898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1207da56-47eb-4035-b16b-dafc6dd97ca2 · outbound

This paper cites Digital twin in the iot context: A survey on technical features, scenarios, and architectural models,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Digital twin in the iot context: A survey on technical features, scenarios, and architectural models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:23.018139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 611077ef-653e-4bf3-ac59-ea1fa7e83906 · outbound

This paper cites Digital twins from a networking perspective,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Digital twins from a networking perspective,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:22.772971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 56f7511b-beba-4281-a9b4-7f40608f98f3 · outbound

This paper cites Dynamic offloading for edge computing-assisted metaverse systems,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Dynamic offloading for edge computing-assisted metaverse systems,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:22.571557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 42b52f66-c7c1-43e1-9b47-e7684daea4c2 · outbound

This paper cites Deep reinforcement learning with double q-learning,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Deep reinforcement learning with double q-learning,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:23:22.174509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32573c7b-b8e1-4a31-9dd2-1b0521589302 · outbound

This paper cites Optimizing communication and computation for multi-uav in- formation gathering applications,.

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Optimizing communication and computation for multi-uav in- formation gathering applications,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:23:22.408627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.