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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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2309.07418.

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

pith.paper-citation-record.v1
2309.07418 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 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:51:12.427596Z

measured 0 of 1 external citation measurements

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

source=pdf_text observed=2026-05-17T18:00:50.053377Z digest=sha256:80da94ed7e13233f6d127c98cac67ccea9836d3f2a1607deb283c6da0a115ae0

Observation 1586c74b-fec8-4e79-af17-e626c4bab00e · inbound

Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation cites this paper.

Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-09T18:51:12.427596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:51:12.427596Z digest=sha256:2068f134cda71b483d5529caf6fd2c6967ead0b16b4013766432152c6e031f18

Observation 9fc34ebd-3520-4c75-9248-204fe52be610 · inbound

High-Order Matching for One-Step Shortcut Diffusion Models cites this paper.

High-Order Matching for One-Step Shortcut Diffusion 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 2014

Resolution
unresolved
no resolver link, observed 2026-08-09T18:10:53.050576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:10:53.050576Z digest=sha256:5f36baaf15918cd42174e5a270a6f498b24a18adb57be55f0fa68eb65752f835

Observation b608b22e-dbe5-4319-80d7-cc00511d4b8e · inbound

Universal Approximation of Visual Autoregressive Transformers cites this paper.

Universal Approximation of Visual Autoregressive Transformers A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-08T16:39:14.153603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:39:14.153603Z digest=sha256:766139d5b98b8dfed3caeb77ad7e421e6e3836a79b89a41b370089918da825d4

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.

source=arxiv_source observed=2026-08-07T15:42:52.220796Z digest=sha256:2dc22dd68ee814de23bdfc7e4b4714c3b069686defc2dfe3347e0fd3d94f8ece

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.

source=pdf_text observed=2026-08-06T23:04:03.550004Z digest=sha256:cf09c215cad081a3b59c380755aca5c6895b799e2794f181df9b728384855f0f

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

source=arxiv_source observed=2026-06-27T22:32:56.444150Z digest=sha256:b267ed019c770239cdc9a11865e8a0e2f40805a93e9d098e48611e411626403e