Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2309.07418.
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 2026-08-07T15:42:52.220796Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:37:09.296789Z
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 41c6058a-fc62-4869-adc5-76ba5f75b3c6 · inbound
H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models cites this paper.
H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
Reference 111
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T18:00:50.319286Z
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 477ac53f-f88c-4aa6-ab43-da5744a65aa8 · inbound
Subquadratic Algorithms and Hardness for Attention with Any Temperature cites this paper.
Subquadratic Algorithms and Hardness for Attention with Any Temperature A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:42:52.220796Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 44853712-05b8-401f-ade7-0b627ecb60f1 · inbound
Accept More, Reject Less: Reducing up to 19% Unnecessary Desk-Rejections over 11 Years of ICLR Data cites this paper.
Accept More, Reject Less: Reducing up to 19% Unnecessary Desk-Rejections over 11 Years of ICLR Data A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:04:03.550004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 892184b8-f20f-4656-958a-3a9e40760422 · inbound
Attention at the Theoretical Minimum: A Mathematics of Arrays Framework for Memory-Optimal Transformer Kernels cites this paper.
Attention at the Theoretical Minimum: A Mathematics of Arrays Framework for Memory-Optimal Transformer Kernels A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:37:09.298326Z
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.