Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:38:45.201407Z
Paper Citation Record · LEDGER
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
As of 11 August 2026, this Paper Citation Record lists 4 of 4 outbound references and 0 inbound Pith citation observations for arXiv:2501.15129.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
4 of 4 outbound references displayed
- verified exact0
- verified fuzzy1
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cb914885-09ae-4574-ae97-acd2eadfd7e6 · outbound
This paper cites In Proceedings of International Conference on Machine Learning (ICML).
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning In Proceedings of International Conference on Machine Learning (ICML)
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:38:45.382459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a8b9002d-18ba-4dfe-87c0-92ed9df22e38 · outbound
This paper cites Reinforcement Learning for Market Making in a Multi-agent Dealer Market.
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Reference 2012
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:38:45.196668Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fa49a912-68d9-4b3e-8852-1f20836928f5 · outbound
This paper cites Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies.
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies
Reference 2016
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-10T14:38:45.347953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b725b8b9-f6d9-4349-a25b-658a27f77880 · outbound
This paper cites Evolving Rewards to Automate Reinforcement Learning.
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning Evolving Rewards to Automate Reinforcement Learning
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:38:45.186797Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.