Paper Citation Record · LEDGER

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions

As of 11 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2604.09523.

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

pith.paper-citation-record.v1
2604.09523 v3

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:32:11.717573Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9754082-bd99-473e-ade1-e83958cb05cf · outbound

This paper cites Intelligent simulation of APT operational trajec- tories.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Intelligent simulation of APT operational trajec- tories

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.641790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95d1798e-6bd1-4d99-8bb5-dcac4d1e421f · outbound

This paper cites Reinforcement learning in continuous time: Advantage updating.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Reinforcement learning in continuous time: Advantage updating

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.646673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c07efa17-a29d-486a-bf52-54aae763845f · outbound

This paper cites Machine learning cybersecurity: bridging the sim2real gap.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Machine learning cybersecurity: bridging the sim2real gap

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.650768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aa2675e-592c-420a-9784-11f5d38eb5bb · outbound

This paper cites Neural ordinary differential equations.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Neural ordinary differential equations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.654983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f635bde3-7426-4c80-bcfc-308eb9e186a3 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.659378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 637bafdb-d9ea-48bf-830d-9b7319258953 · outbound

This paper cites Reinforcement learning in continuous time and space.Neural computation, 12(1):219–245, 2000.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Reinforcement learning in continuous time and space.Neural computation, 12(1):219–245, 2000

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.664276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f73c18-e396-4cf7-acca-545d19f8a7e8 · outbound

This paper cites Graph convolu- tional reinforcement learning.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Graph convolu- tional reinforcement learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.668940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c425bca3-02b4-40b5-a3b8-8d84b6d41345 · outbound

This paper cites Planning and acting in partially observable stochastic domains.Artificial intelligence, 101(1-2):99–134, 1998.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Planning and acting in partially observable stochastic domains.Artificial intelligence, 101(1-2):99–134, 1998

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.672729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 805a167a-05cb-4bda-8e5d-6020eaa15ea2 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive envi- ronments.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Multi-agent actor-critic for mixed cooperative-competitive envi- ronments

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.676654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee659d63-5bcd-4f51-b543-c86ec04404b0 · outbound

This paper cites QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.680508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f7c007-b509-47f7-97db-0cbdebe700e8 · outbound

This paper cites Zero trust ar- chitecture.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Zero trust ar- chitecture

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.684555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5693884-1df3-4466-aef6-abffc74d18ee · outbound

This paper cites Latent ordinary differential equations for irregularly-sampled time series.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Latent ordinary differential equations for irregularly-sampled time series

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.688394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e74aee8e-8673-4339-b9a6-3e5f838ba1c4 · outbound

This paper cites Proximal Policy Optimization Algorithms.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Proximal Policy Optimization Algorithms

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.692499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e38b82b4-6b82-4554-9984-9ae0d27e45fd · outbound

This paper cites NASim: Network attack sim- ulator.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions NASim: Network attack sim- ulator

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.696691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af239d2a-8572-4417-956b-28c2c15d53ad · outbound

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

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions CybORG: A Gym for the Development of Autonomous Cyber Agents

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.700729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faae47dc-6882-4eea-842f-387206064aa1 · outbound

This paper cites MITRE ATT&CK: Design and philosophy.Technical report, The MITRE Corporation, 2018.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions MITRE ATT&CK: Design and philosophy.Technical report, The MITRE Corporation, 2018

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.705273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22e9e599-435d-48a0-a331-c5ae71f371bc · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions Domain randomization for transferring deep neural networks from simulation to the real world

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.709419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ee2c9b0-7a38-4436-bc2f-4b73a826b9f5 · outbound

This paper cites MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in Neural Information Processing Systems, 33:5776–5788, 2020.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.Advances in Neural Information Processing Systems, 33:5776–5788, 2020

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.713529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b2fd41-c6b4-468a-b012-16520a931fb9 · outbound

This paper cites The surprising effectiveness of PPO in cooperative multi-agent games.

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions The surprising effectiveness of PPO in cooperative multi-agent games

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T16:32:11.717573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.