Paper Citation Record · LEDGER

Towards understanding how attention mechanism works in deep learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.18288.

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

pith.paper-citation-record.v1
2412.18288 v1

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:05:02.502481Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:56:15.441801Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 fe4b9dc8-3e46-43ed-9d3a-6f8ce3ca128e · inbound

Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs cites this paper.

Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs Towards understanding how attention mechanism works in deep learning

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-05T20:59:36.897052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02f2568a-cf97-47ad-8fe9-c5fa3b1a24af · inbound

Attention's forward pass and Frank-Wolfe cites this paper.

Attention's forward pass and Frank-Wolfe Towards understanding how attention mechanism works in deep learning

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-05T21:05:02.502481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5745ca25-4c14-4d87-9b02-61d40e725aa0 · inbound

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation Towards understanding how attention mechanism works in deep learning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:01:46.313882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf9ec9b5-68dd-413d-ac48-f943d92837ce · inbound

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation Towards understanding how attention mechanism works in deep learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-05T10:25:17.199621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1740aba-fbd5-4a2a-bea8-58baccbf15cd · inbound

MomentKV: Closing the Directional Gap in KV Cache Eviction for Long-Context Inference cites this paper.

MomentKV: Closing the Directional Gap in KV Cache Eviction for Long-Context Inference Towards understanding how attention mechanism works in deep learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:56:15.443916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4368ff3f-46d2-4554-b36c-331c141bbd05 · inbound

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers cites this paper.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Towards understanding how attention mechanism works in deep learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T10:03:27.747512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.