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Paper Citation Record · LEDGER

Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.16410.

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

pith.paper-citation-record.v1
2306.16410 v1

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:07:50.854955Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T20:18:15.596066Z

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 b8bad57a-08cb-4d1f-a0c4-6a097a15bc29 · inbound

BLINK: Multimodal Large Language Models Can See but Not Perceive cites this paper.

BLINK: Multimodal Large Language Models Can See but Not Perceive Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-15T20:18:15.599633Z

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.

source=pdf_text observed=2026-05-15T20:18:15.439163Z digest=sha256:a83a3a0ad631ae497a1575265847d95ec40c78caf7af9c0ecf6f4e748312fcaf

Observation 9eb749c3-749c-4af5-af53-c63d26807a07 · inbound

EVL-MCoT: Enhanced Vision-Language Multi-CoT for Harmful Meme Detection cites this paper.

EVL-MCoT: Enhanced Vision-Language Multi-CoT for Harmful Meme Detection Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-01T06:07:50.854955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:07:50.854955Z digest=sha256:e0f028a63200d3af5a453903bbf2954f1fcf803c5425f948276a6f64d44c4aa9