Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.07745.
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 2026-08-07T14:10:39.668054Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T08:11:03.793696Z
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 4d245b6c-588c-43c5-a0a9-3dbff3007d34 · inbound
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications cites this paper.
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:10:39.668054Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e59cce12-d689-448e-8a01-45cb8a382526 · inbound
Building Better Environments for Autonomous Cyber Defence cites this paper.
Building Better Environments for Autonomous Cyber Defence Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:11:03.806092Z
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 5c6e0014-b882-4034-9065-e6d34a80fed2 · 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 Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T01:39:53.626474Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.