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Paper Citation Record · LEDGER

Vision Transformers with Mixed-Resolution Tokenization

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.00287.

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

pith.paper-citation-record.v1
2304.00287 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:43.816592Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:07:42.289240Z

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 5344084a-1c81-409f-84ca-60a6f79cc2ad · inbound

CAT: Content-Adaptive Image Tokenization cites this paper.

CAT: Content-Adaptive Image Tokenization Vision Transformers with Mixed-Resolution Tokenization

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-10T21:56:43.816592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:56:43.816592Z digest=sha256:ef7a10c34e044e18745f9f4bf2b20bd023b4b0064f6ffff20acf28b366f0549a

Observation b70de1e4-31e7-4325-9763-547d31e2aca4 · inbound

SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language Modeling cites this paper.

SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language Modeling Vision Transformers with Mixed-Resolution Tokenization

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:07:42.339253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-05T18:07:40.279621Z digest=sha256:6539b1819a32b38344f7b79612c15090523c706149cfa1f890e1bef11364a668