Paper Citation Record · LEDGER

Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium

As of 10 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.

pith.paper-citation-record.v1
2411.15036 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:25:59.949703Z

measured 0 of 1 external citation measurements

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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.