Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.10644.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T12:25:42.624361Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-14T22:08:15.405681Z
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 4916a4ee-203f-4de1-a64a-2de066950051 · inbound
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality cites this paper.
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:16:25.581876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d97c8a28-45ff-4032-99ac-3f7f1b598365 · inbound
Titans: Learning to Memorize at Test Time cites this paper.
Titans: Learning to Memorize at Test Time Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T22:08:15.409134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d1565af2-adb7-4c74-b980-67bf83a13cfe · inbound
LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid cites this paper.
LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T12:25:42.624361Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.