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Paper Citation Record · LEDGER

Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.18066.

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

pith.paper-citation-record.v1
2407.18066 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:41:15.911526Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:04:46.850428Z

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 2268dd02-ac26-460e-bafa-053fdcd1bde8 · inbound

Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems cites this paper.

Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-07T11:41:15.911526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:15.911526Z digest=sha256:cf5bf779865ffdc0946edccbb663bbf2c412e95419496750622e1eccf8baf433

Observation 656e5ded-6741-4e17-90e5-35a82da2daf7 · inbound

Resilience under Uncertainty: Securing 6G through Stochastic Reinstantiation of RAN Functions cites this paper.

Resilience under Uncertainty: Securing 6G through Stochastic Reinstantiation of RAN Functions Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-19T14:33:09.517762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T14:32:57.767535Z digest=sha256:210697268244305a86259afe6ae6dab82d078bf659f49ae0571ee812208a7de7

Observation f181e085-2099-4a18-8ad0-d78b491957c0 · inbound

Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning cites this paper.

Joint Outage Detection and Compensation for Self-Healing 5G RAN via Deep Reinforcement Learning Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:04:46.851951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T05:12:27.640022Z digest=sha256:57851509bc6b888204e03eb5b93718101ab65e42fc0a0f13f04be086925a3a2e