Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2310.02980.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T10:22:53.226128Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
5
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5055d1f2-ae73-4e64-a994-08493ee3d8c7 · inbound
Rethinking the long-range dependency in Mamba/SSM and transformer models cites this paper.
Rethinking the long-range dependency in Mamba/SSM and transformer models Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:22:53.226128Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b9e919f-3e08-43b3-8159-5e3210846d33 · inbound
Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention cites this paper.
Stochastic Attention: Connectome-Inspired Randomized Routing for Expressive Linear-Time Attention Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T22:53:23.337094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 306a66f2-6645-4293-95c6-b49bd81dcc37 · inbound
Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation cites this paper.
Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Reference 1
- Resolution
- verified exact
- orphan_title_repair, observed 2026-05-13T17:19:19.191141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e45f6131-bc0e-4659-a505-22a32fb7b383 · inbound
Continuity Laws for Sequential Models cites this paper.
Continuity Laws for Sequential Models Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Reference 43
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T07:56:26.715323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 29692a8f-1d9e-4f0a-a3f9-49ba7d2e96b3 · inbound
The Importance of Encoder Choice:A Tabular-Image Study cites this paper.
The Importance of Encoder Choice:A Tabular-Image Study Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Reference 156
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T19:07:35.105495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.