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Paper Citation Record · LEDGER

Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.20568.

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

pith.paper-citation-record.v1
2405.20568 v2

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 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:06:11.404203Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:47:00.216529Z

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 14b8c700-501c-4c10-9bed-1a92b3b400dd · inbound

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks cites this paper.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T18:06:11.404203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:06:11.404203Z digest=sha256:1b3fdf3a89a22d79a55074ae9a2338760fb3df38a600240fe273716dbc668e97

Observation e35b1ebb-f9e0-48a7-8990-da3aeeed7b89 · inbound

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles cites this paper.

Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-08T10:26:15.737552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:26:15.737552Z digest=sha256:bf59e7fd215b5f2c1b01a0bda016de0f9b450040b8dd1a3c84f3514866b094ed

Observation a3278172-82ad-4df5-bbf9-82b3f867e539 · inbound

UAV-Assisted Integrated Communication and Over-the-Air Computation with Interference Awareness cites this paper.

UAV-Assisted Integrated Communication and Over-the-Air Computation with Interference Awareness Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:47:00.303079Z

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=2026-08-06T19:46:59.118326Z digest=sha256:27d3c40f054951efd9e6160b315e2e0b2af1ff68e66dbcf25438d1a4d9f9c9b9