Paper Citation Record · LEDGER

What Makes Convolutional Models Great on Long Sequence Modeling?

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2210.09298.

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

pith.paper-citation-record.v1
2210.09298 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:49:52.659019Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, 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

20
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6ae29ffd-fcbe-402b-98c5-96a15e07e5c6 · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.592089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a86b3051-3238-40c5-b68c-7d4911d25845 · inbound

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing cites this paper.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T22:52:33.489223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cefe63ef-ecd6-4495-a063-88fd25eaa985 · inbound

A Survey on Mamba Architecture for Vision Applications cites this paper.

A Survey on Mamba Architecture for Vision Applications What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T13:40:59.367565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d50740fe-bb87-495a-9942-50554b8c5034 · inbound

GeoMaNO: Geometric Mamba Neural Operator for Partial Differential Equations cites this paper.

GeoMaNO: Geometric Mamba Neural Operator for Partial Differential Equations What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:52.659019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3d2857-5ce9-469c-ad0b-4e0326c625c9 · inbound

CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model cites this paper.

CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:17:16.277748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 02cb3960-f4e1-47ac-9e14-a669243faa94 · inbound

Towards Understanding Self-Pretraining for Sequence Classification cites this paper.

Towards Understanding Self-Pretraining for Sequence Classification What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:33:58.271324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9570fd65-0441-4fa7-a7c7-a00e6c151934 · inbound

Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models cites this paper.

Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models What Makes Convolutional Models Great on Long Sequence Modeling?

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:39:29.494254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.