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Paper Citation Record · LEDGER

Adaptive Attention Span in Transformers

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1905.07799.

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

pith.paper-citation-record.v1
1905.07799 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:10:29.910010Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:46:16.558617Z

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 6b69cc75-396e-4345-b801-736e02e7caf3 · inbound

Adaptively Sparse Transformers cites this paper.

Adaptively Sparse Transformers Adaptive Attention Span in Transformers

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-14T10:10:29.910010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:10:29.910010Z digest=sha256:1904bf9235d599fe2b285ca7a0789de0e760e6b66816ef33cbc7306748513652

Observation f9bcd4de-875f-426d-bffa-9045c64bd324 · inbound

Compressive Transformers for Long-Range Sequence Modelling cites this paper.

Compressive Transformers for Long-Range Sequence Modelling Adaptive Attention Span in Transformers

Reference 121

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:46:16.561868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-18T10:46:16.373197Z digest=sha256:aaf91f5c877cc7711cffcfc3db03baff9ea5f40f46eb86224da8eba47a6c5e73

Observation 41819486-1c1f-4473-8542-0e56ebf949ed · inbound

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity cites this paper.

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity Adaptive Attention Span in Transformers

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T23:57:11.125835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-12T23:57:10.953617Z digest=sha256:b73b624adb3ba8ff4c3526b9ab8e46600b6befda559bb78bd7a13f843bdea6d6

Observation 77d7c5f8-5577-49ad-a511-3c075d008c32 · inbound

ST-MoE: Designing Stable and Transferable Sparse Expert Models cites this paper.

ST-MoE: Designing Stable and Transferable Sparse Expert Models Adaptive Attention Span in Transformers

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-05-12T23:14:25.540984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-12T23:14:25.431471Z digest=sha256:89800dfbb99bb7ed390a3d7af9bd9323f4b4b889dbf6c2cd9cb1cce1ab9454b2

Observation 82fbe1a1-2409-4cbd-a705-a93b3fa64084 · inbound

ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction cites this paper.

ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction Adaptive Attention Span in Transformers

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T18:31:59.347955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:31:59.347955Z digest=sha256:e958988cacef667a16ed276a01347bbc692a8423118f52a50481e907e739d68b

Observation 1374c012-f5cb-46d4-af7a-b3a17fa0cd58 · inbound

Change of Thought: Adaptive Test-Time Computation cites this paper.

Change of Thought: Adaptive Test-Time Computation Adaptive Attention Span in Transformers

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T16:28:21.514924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:28:21.514924Z digest=sha256:8cc33f77961ad44bb1ba1fccb7a80fad4c973a2136fd8af0fd1a3208a376866f

Observation 5381ff8e-c8a9-4738-95c8-afc06896dcd8 · inbound

MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security cites this paper.

MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security Adaptive Attention Span in Transformers

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-04T23:09:41.550995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T23:09:41.550995Z digest=sha256:ce29acda971a764e7fd6ac4ecfe10d4c8d5f6528fc746ff8759e8c6a2f51f88b

Observation 0b53df93-eb51-4197-9e8a-252adf6e5f7f · inbound

LPC-SM: Local Predictive Coding and Sparse Memory for Long-Context Language Modeling cites this paper.

LPC-SM: Local Predictive Coding and Sparse Memory for Long-Context Language Modeling Adaptive Attention Span in Transformers

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T11:25:31.070739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-15T11:22:08.937342Z digest=sha256:64f0d5cd1965acffc17b820f4d5424d4f710bbb5e276c8de53ea342b0819c880

Observation 6af0970f-431c-4453-b421-83f15b490dd3 · inbound

Compute Where it Counts: Self Optimizing Language Models cites this paper.

Compute Where it Counts: Self Optimizing Language Models Adaptive Attention Span in Transformers

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:46:29.377109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-12T04:57:58.649358Z digest=sha256:7e3b351cb755878c5ccdb7b5f4e8d864cc0729a3ef1b1050ceafdfe99bedba93