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.

pith.paper-citation-record.v1
2607.05939 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-08T20:06:44.239815Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.