Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2401.14489.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:04:38.067249Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T17:46:47.122915Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 2f55f682-927c-4488-8b49-ae682b886d55 · inbound
The Zamba2 Suite: Technical Report The Case for Co-Designing Model Architectures with Hardware
Reference 2
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ff3e5e3-ccd7-4387-ae8b-f85ad31f1a9d · inbound
Best Practices for Large Language Models in Radiology The Case for Co-Designing Model Architectures with Hardware
Reference 53
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1caf2a2f-444a-4beb-aac8-736186efbcc2 · inbound
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference The Case for Co-Designing Model Architectures with Hardware
Reference 115
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b8d73282-b716-4528-85b6-a516858600f3 · inbound
Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences The Case for Co-Designing Model Architectures with Hardware
Reference 17
Source-reported events for the cited work
Unavailable: canonical work link unavailable.