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Paper Citation Record · LEDGER

Generative AI Enabled Matching for 6G Multiple Access

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.04137.

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

pith.paper-citation-record.v1
2411.04137 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:01:03.773374Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T16:45:56.718868Z

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 a7cb82c0-040f-4816-9d48-4dd08ba6935c · inbound

Graph Diffusion-Based AeBS Deployment and Resource Allocation in RSMA-Enabled URLLC Low-Altitude Wireless Networks cites this paper.

Graph Diffusion-Based AeBS Deployment and Resource Allocation in RSMA-Enabled URLLC Low-Altitude Wireless Networks Generative AI Enabled Matching for 6G Multiple Access

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T20:01:03.773374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:01:03.773374Z digest=sha256:867b4e0e6cd2e1e3ad8fb2ac871067bf4395dc396874e70a75f19a098e1f3d17

Observation 8f659c0d-7877-4224-8c8c-ebb8d0fc3432 · inbound

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning cites this paper.

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning Generative AI Enabled Matching for 6G Multiple Access

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-06T16:45:56.783714Z

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.

source=pdf_text observed=2026-08-06T16:45:53.537181Z digest=sha256:ed6a4734723469a98788aafc131cc4fdf4446f52d752ca4f699312a9ec9ee46d