Pith. sign in

Paper Citation Record · LEDGER

Audio Transformers

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.00335.

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

pith.paper-citation-record.v1
2105.00335 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:12:15.109369Z

measured 0 of 1 external citation measurements

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 b9768811-1303-4817-bef2-42bee0aac3f7 · inbound

The iNaturalist Sounds Dataset cites this paper.

The iNaturalist Sounds Dataset Audio Transformers

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

source=pdf_text observed=2026-08-07T12:12:15.109369Z digest=sha256:c73b3a15ecf3235c0f3c3bc6d5d459fff70e564c5574bf364f50a901b68c9e97

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.

source=pdf_text observed=2026-07-31T05:06:55.171151Z digest=sha256:53c9fe48f193b2f41ed1bf72ad70acd6180bce50915c7d6a6173959a16a5e006