Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2012.03204.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T15:00:07.967882Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T11:24:46.431558Z
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 e9cee1c4-38e1-4fe1-a281-c4f7f4ba7bd5 · inbound
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement cites this paper.
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:00:07.967882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 83f62352-0b06-40bf-8cd0-650cb4b373f2 · inbound
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning cites this paper.
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T11:24:46.436976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.