Paper Citation Record · LEDGER

Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.07745.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.07745 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:03:18.529361Z

measured 0 of 1 external citation measurements

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 2d0ffab0-ab03-4636-a672-670580b5b2a2 · inbound

An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents cites this paper.

An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T21:03:18.529361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-10T06:31:04.303077+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.