Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.02581.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:47:00.944360Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T14:49:54.087367Z
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 a43bbc73-10b0-4d48-afaf-00791437763e · inbound
Symmetries-enhanced Multi-Agent Reinforcement Learning cites this paper.
Symmetries-enhanced Multi-Agent Reinforcement Learning Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:47:00.944360Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3860828f-80fe-4207-b27e-97445fec5a25 · inbound
cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending cites this paper.
cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T14:49:54.095788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.