Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.20067.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:10:22.365457Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-09T06:00:36.039447Z
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 39163fcf-0cbe-496c-a08c-d6efa405b086 · inbound
Minimax-Optimal Multi-Agent Robust Reinforcement Learning cites this paper.
Minimax-Optimal Multi-Agent Robust Reinforcement Learning Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
Reference 1953
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:10:22.365457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d21c175-7920-41d2-969f-a03eb31c9303 · inbound
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation cites this paper.
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T06:00:36.042169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.