Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-08T20:06:44.239815Z
Paper Citation Record · LEDGER
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning
As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.05939.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
21 of 21 outbound references displayed
- verified exact4
- verified fuzzy17
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation dc762d5f-5ed8-43b9-9404-8a2deeef831b · outbound
This paper cites Survey on Anti-Drone Systems: Components, Designs, and Challenges,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Survey on Anti-Drone Systems: Components, Designs, and Challenges,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.767896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a5037853-ba1e-41ea-9b51-8e874ba30be6 · outbound
This paper cites Yanushevsky, Modern Missile Guidance , 2nd ed.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Yanushevsky, Modern Missile Guidance , 2nd ed
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.771763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9b72a411-d743-4a1c-81a0-078adfa71eec · outbound
This paper cites Search and pursuit-evasion in mobile robotics,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Search and pursuit-evasion in mobile robotics,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.764407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 10b339e5-4cfa-471b-bdeb-fd7b8de50754 · outbound
This paper cites Champion-level drone racing using deep rein- forcement learning,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Champion-level drone racing using deep rein- forcement learning,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.775589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 31cd2766-f18f-459b-a7a1-41661e4eb82e · outbound
This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T20:15:34.402336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0753c64d-1d23-4757-a1c2-a64d3c74b950 · outbound
This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Dota 2 with Large Scale Deep Reinforcement Learning
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T20:15:34.409658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 25b7a757-44b0-49ae-ba7a-f8d6801576ac · outbound
This paper cites Towards safe mid-air drone interception: Strategies for tracking & capture,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Towards safe mid-air drone interception: Strategies for tracking & capture,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.762153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 93244f28-f8e2-4c5c-9e3f-33cdca4de55f · outbound
This paper cites Optimal vehicle- target assignment: A swarm of pursuers to intercept maneuvering evaders based on ideal proportional navigation,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Optimal vehicle- target assignment: A swarm of pursuers to intercept maneuvering evaders based on ideal proportional navigation,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.766204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6f4f1104-9bee-4e89-b594-006781241462 · outbound
This paper cites Distributed guidance for interception by using multiple rotary-wing unmanned aerial vehicles,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Distributed guidance for interception by using multiple rotary-wing unmanned aerial vehicles,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.769880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 55071241-380a-4bc7-9878-09cf78a1b7f1 · outbound
This paper cites Cooperative pursuit with voronoi partitions,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Cooperative pursuit with voronoi partitions,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.785086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 685ff80c-b492-44ea-8d34-85f3b2b1a92c · outbound
This paper cites Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T20:15:34.404702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e06ad10c-fcf6-4df4-a825-ace4fe1d0c14 · outbound
This paper cites Game of Drones: Multi-UA V Pursuit-Evasion Game With Online Motion Planning by Deep Reinforcement Learning,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Game of Drones: Multi-UA V Pursuit-Evasion Game With Online Motion Planning by Deep Reinforcement Learning,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.777589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8c3b587c-8ea7-415e-b147-da6bab0b55ba · outbound
This paper cites Dacoop-a: Decentralized adaptive cooperative pursuit via attention,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Dacoop-a: Decentralized adaptive cooperative pursuit via attention,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.760261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 28c35295-e7a5-4ace-9588-f80a43104a2f · outbound
This paper cites Online planning for multi-uav pursuit-evasion in unknown environments using deep reinforcement learning,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Online planning for multi-uav pursuit-evasion in unknown environments using deep reinforcement learning,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.783182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 82b90133-6631-4a5e-8194-3eb6dfa03db7 · outbound
This paper cites Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T20:15:34.407222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9cabddfb-2924-41ee-a0da-355f911562a7 · outbound
This paper cites Agile interception of a flying target using competitive reinforcement learning,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Agile interception of a flying target using competitive reinforcement learning,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.754497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2da5b09a-f785-4785-93c8-4e122a4e61f0 · outbound
This paper cites Multi-Agent Actor-Critic for Mixed Cooperative- Competitive Environments,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Multi-Agent Actor-Critic for Mixed Cooperative- Competitive Environments,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.758483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 30dbe11d-3654-43e0-bbec-24e405b3dd20 · outbound
This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Grandmaster level in starcraft ii using multi-agent reinforcement learning,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.756592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0b9b88ac-e4a7-4088-8587-102cf77372d7 · outbound
This paper cites The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.781511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 49a207fe-8db8-4aee-bf2d-617e6381d9c0 · outbound
This paper cites Adaptive incremental nonlinear dynamic inversion for attitude control of micro air vehicles,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning Adaptive incremental nonlinear dynamic inversion for attitude control of micro air vehicles,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.773925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f259eb00-50b2-47e0-8fa4-b0ee1821c054 · outbound
This paper cites JAX: composable transformations of Python+NumPy programs,.
Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning JAX: composable transformations of Python+NumPy programs,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T20:25:37.779772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.