Paper Citation Record · LEDGER

Nash Q-Network for Multi-Agent Cybersecurity Simulation

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2509.00678.

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

pith.paper-citation-record.v1
2509.00678 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:25:59.994792Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy17
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d0b33f9c-321f-4f19-a4ad-335bcc4d45c6 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Mastering the game of go with deep neural networks and tree search,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.444103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58e3bcf5-7b2a-4f4a-b40d-cf899842ed07 · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Grandmaster level in starcraft ii using multi-agent reinforcement learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.890372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 654eb9cc-baa9-4716-9d03-a01492134ddb · outbound

This paper cites Nash q-learning for general-sum stochastic games,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Nash q-learning for general-sum stochastic games,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.419153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6739e7ce-1158-4286-841c-bb0de738dd03 · outbound

This paper cites CybORG: A Gym for the Development of Autonomous Cyber Agents.

Nash Q-Network for Multi-Agent Cybersecurity Simulation CybORG: A Gym for the Development of Autonomous Cyber Agents

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.900077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf402e31-f856-4e8f-8e9e-4ca5495a6105 · outbound

This paper cites Markov games as a framework for multi-agent reinforcement learning,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Markov games as a framework for multi-agent reinforcement learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.403467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a11558df-aaa1-4c02-a5b8-3504999e5596 · outbound

This paper cites Markov perfect equilibrium in a repeated principal-agent rela- tionship,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Markov perfect equilibrium in a repeated principal-agent rela- tionship,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.387607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 660e8977-5268-4bde-9b3b-1a897a2692ab · outbound

This paper cites A survey of game theory as applied to network security,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation A survey of game theory as applied to network security,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.371273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e76ac92-a337-48c5-bae1-0506ba3f02b9 · outbound

This paper cites The complexity of computing a nash equilibrium,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation The complexity of computing a nash equilibrium,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.919127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47ba5eb8-7431-4792-a86f-2b4bd112a522 · outbound

This paper cites A comprehensive survey of multiagent reinforce- ment learning,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation A comprehensive survey of multiagent reinforce- ment learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.344338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 791f92c5-7d29-4357-bcfe-32805ab67298 · outbound

This paper cites A deep learning-based multi-agent system for intrusion detection,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation A deep learning-based multi-agent system for intrusion detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.328910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1536871c-4b62-4224-b043-26e0a91e9ca7 · outbound

This paper cites Deep reinforcement learning for adaptive cyber defense in network security,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Deep reinforcement learning for adaptive cyber defense in network security,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.312338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 268408f9-a9c1-48cf-b09c-44dcfedf9ec3 · outbound

This paper cites Reinforcement learning for efficient network penetration testing,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Reinforcement learning for efficient network penetration testing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.295366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc8725fc-5fec-4e80-9946-8621ad8eff54 · outbound

This paper cites Combining deep reinforcement learning and search for imperfect-information games,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Combining deep reinforcement learning and search for imperfect-information games,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.274921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3f39add-fc6a-4cd0-a7fd-6abbe4244ead · outbound

This paper cites Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.949703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c50cc2f2-d6a6-4d23-9237-2f8437e50846 · outbound

This paper cites A comprehensive systematic literature review on intrusion detection systems,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation A comprehensive systematic literature review on intrusion detection systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.257456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3cea5c4b-63d2-4098-8b7e-415fc20a21d2 · outbound

This paper cites Correlated q-learning,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Correlated q-learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.240980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 83fe8b63-e8fd-4d44-8fdd-c4361c9d43d6 · outbound

This paper cites Alpcan and T.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Alpcan and T

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.222553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bac529b3-d118-43e0-a6da-8002aa74cc69 · outbound

This paper cites Deep q-learning for nash equilibria: Nash-dqn,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Deep q-learning for nash equilibria: Nash-dqn,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.201116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 619f91d3-2eb0-4a11-8fcb-73c5d162bd0c · outbound

This paper cites Equilibrium points of bimatrix games,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Equilibrium points of bimatrix games,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.971718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a2a5f4-afb0-4d2e-b95d-8162a1f50534 · outbound

This paper cites Ray: A distributed framework for emerging{AI} applications,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Ray: A distributed framework for emerging{AI} applications,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.164551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 74578041-7280-4e3b-ab31-5f30c4d06c35 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Adam: A Method for Stochastic Optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.981069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4e2f42a-cbbf-4ef5-a294-dbad97247885 · outbound

This paper cites On Autonomous Agents in a Cyber Defence Environment.

Nash Q-Network for Multi-Agent Cybersecurity Simulation On Autonomous Agents in a Cyber Defence Environment

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T13:25:59.985731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b93b0a16-c381-438c-b80f-1aa0ad38d38f · outbound

This paper cites Autonomous network defence using reinforcement learning,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Autonomous network defence using reinforcement learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.147556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fffac843-3bda-41fa-83e2-23177bb0b5d4 · outbound

This paper cites Learning to communicate in multi-agent reinforcement learning for au- tonomous cyber defence,.

Nash Q-Network for Multi-Agent Cybersecurity Simulation Learning to communicate in multi-agent reinforcement learning for au- tonomous cyber defence,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:26:00.127826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.