Paper Citation Record · LEDGER

NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.17246.

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

pith.paper-citation-record.v1
2305.17246 v2

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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:52:20.503515Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T13:11:37.921239Z

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 3ac1687e-a7e9-4b8c-9b21-43966b6f3f2d · inbound

Optimal Security Response to Network Intrusions in IT Systems cites this paper.

Optimal Security Response to Network Intrusions in IT Systems NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Reference 223

Resolution: unresolved; no resolver link, observed 2026-08-09T11:52:20.503515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 576103ca-3a31-422c-9b90-24d69c4f3825 · inbound

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications cites this paper.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:43.095286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04dce863-f4e0-47d7-8e25-56732d291d4c · inbound

Training RL Agents for Multi-Objective Network Defense Tasks cites this paper.

Training RL Agents for Multi-Objective Network Defense Tasks NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:11:38.018967Z

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 be890974-ce76-426e-aa22-2ff732c5acdb · inbound

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation cites this paper.

Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T15:17:40.953276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.