Paper Citation Record · LEDGER

Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.02980.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.02980 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:38.377868Z

measured 1 of 1 external citation measurements

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

Observation 84756198-f321-4955-8a0a-be2dfcada2f1 · inbound

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? cites this paper.

Is Self-Pretraining really useful to improve diagnosis in medical Time Series? Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:38.377868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.