Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.15036.
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 2026-08-05T13:25:59.949703Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T19:18:54.922765Z
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 c3f39add-fc6a-4cd0-a7fd-6abbe4244ead · inbound
Nash Q-Network for Multi-Agent Cybersecurity Simulation cites this paper.
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 bae1ab62-9147-447f-93e5-a9527f5528d3 · inbound
AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning cites this paper.
AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T20:20:11.893594Z
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 cd0a9044-1407-4f35-b428-7bec7352e707 · inbound
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence cites this paper.
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T10:41:30.598171Z
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 17db1211-30c7-4740-8cd2-487243a0d9ba · inbound
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence cites this paper.
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T00:15:09.572662Z
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 c50c573d-5e57-44f2-a865-c205865e6075 · inbound
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence cites this paper.
Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:07:27.582225Z
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 3662727e-a789-4309-92a7-5aa4fa5410bb · inbound
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning cites this paper.
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T19:18:54.924235Z
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.