Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.10863.
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, observed 2026-08-10T20:27:16.914748Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T21:36:15.534138Z
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 773bfda2-fc6a-42b4-8d29-8d420a5fdc86 · inbound
OMEGA: A Low-Latency GNN Serving System for Large Graphs cites this paper.
OMEGA: A Low-Latency GNN Serving System for Large Graphs Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T20:27:16.914748Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb579a6d-b8e6-4087-b74e-1b599e184fac · inbound
PyG 2.0: Scalable Learning on Real World Graphs cites this paper.
PyG 2.0: Scalable Learning on Real World Graphs Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Reference 84
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:03:07.661214Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e625ad1a-b8d7-4aa0-9baf-d699fae15130 · inbound
AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments cites this paper.
AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T21:36:15.535634Z
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.