Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.17246.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:10:43.095286Z
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 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 2026-08-08T06:32:00.761636+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.