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Paper Citation Record · LEDGER

Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.14827.

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

pith.paper-citation-record.v1
2408.14827 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:12:08.337203Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T12:05:23.684697Z

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 29ee9516-46b4-4edb-b041-bd55dea917be · inbound

LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models cites this paper.

LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-10T12:05:23.687489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T04:39:38.431330Z digest=sha256:956d93ce02c64585c375daf1d185a537da451a76bf366fea62fd01ba7098b54d

Observation 91a67848-e16f-48da-b7ad-95038587c70d · inbound

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting cites this paper.

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T00:12:08.337203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:12:08.337203Z digest=sha256:0c6315e602ef03eb37fb5d253af98cd795acc224dddf8d9f71ddc5d61af3710d