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Paper Citation Record · LEDGER

Sparse Attention with Linear Units

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2104.07012.

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

pith.paper-citation-record.v1
2104.07012 v2

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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:01:32.345079Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:25:41.999873Z

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 05e285ee-c4c2-4156-9d1b-733867176421 · inbound

Transformers and Their Roles as Time Series Foundation Models cites this paper.

Transformers and Their Roles as Time Series Foundation Models Sparse Attention with Linear Units

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T05:01:32.345079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T05:01:32.345079Z digest=sha256:1966e03abbc4da4ef625ac5011866eee6fc7db2438d8579dd2935b197d3b1600

Observation e453c126-8e4f-44f8-8a04-502487eb7702 · inbound

Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding cites this paper.

Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding Sparse Attention with Linear Units

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-08T05:29:16.760019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:29:16.760019Z digest=sha256:c2f834154ba57978c0fd292991f3068ec818fe5381b808f9c17b4d9fa9f2ee15

Observation ad62977a-1cd7-4bab-8647-918ca966a4f4 · inbound

Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection cites this paper.

Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection Sparse Attention with Linear Units

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T14:44:47.487054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:44:47.487054Z digest=sha256:14deb19ad2ac6b08e2c97307e3db5f52353e69001ea431feb29d65f498b70353

Observation 0e88edeb-7980-49ae-a051-5e23174cd356 · inbound

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models cites this paper.

Exploring Superposition and Interference in State-of-the-Art Low-Parameter Vision Models Sparse Attention with Linear Units

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T15:28:08.977986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:28:08.977986Z digest=sha256:bf66ad436508417de1c7027c389216d30506b9115555b08c3868692aef715f50

Observation 87f7d3a1-b7a3-4600-90eb-aeb85bea516f · inbound

Empty SPACE: Cross-Attention Sparsity for Concept Erasure in Diffusion Models cites this paper.

Empty SPACE: Cross-Attention Sparsity for Concept Erasure in Diffusion Models Sparse Attention with Linear Units

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-12T03:21:18.780829Z

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.

source=pdf_text observed=2026-05-12T03:19:54.048685Z digest=sha256:2cb9a9c6c9e11a621298d836eeb91c4b4b91f23da0cbd00bb99611e2aa0f5313

Observation fcd9cf5c-ac0f-4e96-954c-92fa868f0771 · inbound

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation cites this paper.

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation Sparse Attention with Linear Units

Reference 37

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:25:42.001428Z

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.

source=pdf_text observed=2026-07-01T05:30:04.016376Z digest=sha256:e50b87e9f9c60ad3b94c6990598db74a4c97b9137e248715006db65239cd80f8