Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.00880.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:14:06.864408Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T12:41:28.389450Z
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 d3a29ef4-d88a-49a7-a2e6-a28970409375 · inbound
Solo Connection: A Parameter Efficient Fine-Tuning Technique for Transformers cites this paper.
Solo Connection: A Parameter Efficient Fine-Tuning Technique for Transformers Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:14:06.864408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 24429093-d47e-454e-8008-2d0942d72c8b · inbound
Principled Curriculum Learning using Parameter Continuation Methods cites this paper.
Principled Curriculum Learning using Parameter Continuation Methods Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T12:41:28.393992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.