Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.10091.
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 2026-08-07T05:57:57.174822Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T19:09:14.099100Z
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 42069d1e-d1a5-4da4-8f3e-cff1bd8a1856 · inbound
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security cites this paper.
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:57:57.174822Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7aba37b-c24b-4741-8c36-486532749ae4 · inbound
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions cites this paper.
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:29:16.402684Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81e7edde-0a42-4c83-9915-ebf748295a5b · inbound
Learning To Communicate Over An Unknown Shared Network cites this paper.
Learning To Communicate Over An Unknown Shared Network Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges
Reference 54
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:09:14.219460Z
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.