Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T10:26:15.751732Z
Paper Citation Record · LEDGER
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles
As of 16 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:2502.08119.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:02:08.309937Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T17:00:40.927890Z
Reference resolution
16 of 16 outbound references displayed
- verified exact0
- verified fuzzy15
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e2cf21e4-c63f-4510-8225-aa7df20d29bd · outbound
This paper cites A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.937118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ea9c9ed2-e96b-48f9-886c-fb6f77d75f85 · outbound
This paper cites Cooperative cognitive dynamic system in UA V swarms: Reconfigurable mechanism and framework,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Cooperative cognitive dynamic system in UA V swarms: Reconfigurable mechanism and framework,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.927149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d93d1f99-592c-44f5-9143-c4a538db77bd · outbound
This paper cites Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Deep reinforcement learning based computation offloading and trajectory planning for multi-UA V cooperative target search,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.917590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 30ee42e6-7fd9-4d10-9693-850c024914ed · outbound
This paper cites Adaptive QoE-aware SFC orchestra- tion in UA V networks: A deep reinforcement learning approach,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Adaptive QoE-aware SFC orchestra- tion in UA V networks: A deep reinforcement learning approach,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.907018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c7215792-64fb-478e-a1ed-44c8096d9ade · outbound
This paper cites Online trajectory and resource optimization for stochastic UA V-enabled MEC systems,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Online trajectory and resource optimization for stochastic UA V-enabled MEC systems,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.897100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f1a166f2-acd5-4a4c-bc8b-14e90711699a · outbound
This paper cites NFV-enabled service recovery in space-air-ground integrated networks: A matching game based approach,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles NFV-enabled service recovery in space-air-ground integrated networks: A matching game based approach,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.886227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cb9f630c-bba8-4794-8ba9-f444c67cd9ea · outbound
This paper cites Collaborative multi-agent deep reinforce- ment learning for energy-efficient resource allocation in heterogeneous mobile edge computing networks,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Collaborative multi-agent deep reinforce- ment learning for energy-efficient resource allocation in heterogeneous mobile edge computing networks,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.877359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7dd78f79-4a0b-449e-87a7-2184a609a5a1 · outbound
This paper cites Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UA Vs cooperative formation system for maritime parallel search,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Integrating dynamic event-triggered and sensor-tolerant control: Application to USV-UA Vs cooperative formation system for maritime parallel search,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.868037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 975e2f8e-95ef-4d3d-8655-78deabecefc9 · outbound
This paper cites 3U: Joint design of UA V-USV-UUV networks for cooperative target hunting,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles 3U: Joint design of UA V-USV-UUV networks for cooperative target hunting,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.858898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ebabb454-1fda-4798-8076-f2052b26421a · outbound
This paper cites Beyond the edge: An advanced exploration of reinforcement learning for mobile edge com- puting, its applications, and future research trajectories,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Beyond the edge: An advanced exploration of reinforcement learning for mobile edge com- puting, its applications, and future research trajectories,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.847961Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 568504cd-fcdf-46de-b47b-27b52610d9b9 · outbound
This paper cites Multi- cluster cooperative offloading for VR task: A MARL approach with graph embedding,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Multi- cluster cooperative offloading for VR task: A MARL approach with graph embedding,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.836601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e35b1ebb-f9e0-48a7-8990-da3aeeed7b89 · outbound
This paper cites Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases.
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.
Observation 8cfcf8cc-4e5e-4b1a-8506-1fcfcb03b322 · outbound
This paper cites On the approximation of cooperative heterogeneous multi-agent reinforcement learning (MARL) using mean field control (MFC),.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles On the approximation of cooperative heterogeneous multi-agent reinforcement learning (MARL) using mean field control (MFC),
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.825774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 15a136ca-7e59-44cb-9648-b6265a486e6c · outbound
This paper cites Joint HAP access and LEO satellite backhaul in 6G: Matching game-based approaches,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Joint HAP access and LEO satellite backhaul in 6G: Matching game-based approaches,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.815073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 902ef5cb-2d2d-4424-a98a-60a56956dbdc · outbound
This paper cites Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.804403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 055d61f4-5e9d-4572-8875-4d452ad726bb · outbound
This paper cites Heterogeneous-agent reinforcement learning,.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles Heterogeneous-agent reinforcement learning,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T10:26:15.792948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
Observation b0f6c6a3-73f9-4995-acef-8df4641c7414 · inbound
Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions cites this paper.
Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles
Reference 128
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:02:08.309937Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ac633fd-aebe-410f-accd-a941e81fefdf · inbound
A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming cites this paper.
A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles
Reference 152
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:00:40.987834Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.