Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Audio Transformers
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2105.00335.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T14:38:49.922737Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T12:12:16.855412Z
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 c3ad2eac-9e10-4e3f-a17e-7b7b4312b389 · inbound
Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers cites this paper.
Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Audio Transformers
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:38:49.922737Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b9768811-1303-4817-bef2-42bee0aac3f7 · inbound
The iNaturalist Sounds Dataset cites this paper.
Reference 92
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:12:16.963449Z
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.
Observation f0cde5c9-b44e-49b8-8a63-c5d0ca5bcf98 · inbound
Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes cites this paper.
Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes Audio Transformers
Reference 96
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T05:06:55.171151Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.