Paper Citation Record · LEDGER

Probing Image-Language Transformers for Verb Understanding

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2106.09141.

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

pith.paper-citation-record.v1
2106.09141 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:21:35.003872Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T16:21:36.477879Z

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 177fad96-974e-474e-9b39-9f75b9d9a461 · inbound

A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks cites this paper.

A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks Probing Image-Language Transformers for Verb Understanding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:21:35.003872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1782759a-7914-4003-aa50-63b0dcad429e · inbound

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens cites this paper.

Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens Probing Image-Language Transformers for Verb Understanding

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T11:15:19.644943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15b44022-7957-458a-960f-75c25060bfaf · inbound

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models cites this paper.

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models Probing Image-Language Transformers for Verb Understanding

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:21:36.481429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b0f3be6-5957-4ccf-8826-e05938a698ef · inbound

HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities cites this paper.

HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities Probing Image-Language Transformers for Verb Understanding

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:11:06.639388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.