Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2310.05939.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:53:46.687032Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T13:56:37.577242Z
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 26361d5e-4662-4947-b479-2fb0ede8e8e4 · inbound
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents cites this paper.
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:56:37.584923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4905b91f-28dd-4037-b269-c74c6a27b433 · inbound
Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence cites this paper.
Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:53:46.687032Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b4a80716-540d-4371-a20c-48041d7085aa · inbound
Adaptive Network Security Policies via Belief Aggregation and Rollout cites this paper.
Adaptive Network Security Policies via Belief Aggregation and Rollout Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T04:57:04.560701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 89a68a46-994c-45b9-9af1-ec74d769e353 · inbound
VulnGym: Evaluating Vulnerability Management Strategies against Advanced Persistent Threats cites this paper.
VulnGym: Evaluating Vulnerability Management Strategies against Advanced Persistent Threats Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T11:52:39.127668Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa1939bf-9ee1-4968-9d9c-87fc36336457 · inbound
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations cites this paper.
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T01:39:55.512107Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.