Paper Citation Record · LEDGER

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

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:38:48.431347Z

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 2026-08-12T06:34:41.77262+00:00.

Observation 9f3ee29b-54d9-4056-b79e-1a3b50a14088 · inbound

Improving Fine-grained Visual Understanding in VLMs through Text-Only Training cites this paper.

Improving Fine-grained Visual Understanding in VLMs through Text-Only Training Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T13:38:48.431347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4282643-22a8-4f6e-97e1-d5321ebdde8b · inbound

A Survey on Large Language Model Acceleration based on KV Cache Management cites this paper.

A Survey on Large Language Model Acceleration based on KV Cache Management Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T00:38:46.109482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d9772ac-1078-4c86-a38a-4694cfe32344 · inbound

Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals cites this paper.

Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T20:10:25.550507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cd63d5-89b2-4ca3-81d8-c1d3e467b73c · inbound

Multi-aspect Knowledge Distillation with Large Language Model cites this paper.

Multi-aspect Knowledge Distillation with Large Language Model Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:28.914427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.