Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
QuaRL: Quantization for Fast and Environmentally Sustainable 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:1910.01055.
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 2026-08-10T16:56:51.009947Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-08T17:19:05.795163Z
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 370fcbf2-6990-4c08-9678-f851544824b3 · inbound
HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation cites this paper.
HEPPO-GAE: Hardware-Efficient Proximal Policy Optimization with Generalized Advantage Estimation QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:56:51.009947Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0f57e02-b9a0-4f2b-884d-0a973fcbb7a4 · inbound
Characterization and Mitigation of ADC Noise by Reference Tuning in RRAM-Based Compute-In-Memory cites this paper.
Characterization and Mitigation of ADC Noise by Reference Tuning in RRAM-Based Compute-In-Memory QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T17:19:05.802862Z
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.