Paper Citation Record · LEDGER

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions

As of 4 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 2 inbound Pith citation observations for arXiv:2511.17722.

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

pith.paper-citation-record.v1
2511.17722 v3

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-17T20:05:20.393575Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T12:19:42.722173Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-20T12:23:17.003497Z

Reference resolution

29 of 29 outbound references displayed

verified exact6
verified fuzzy18
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d50246e3-fae2-4136-a91d-bdb213db25b0 · outbound

This paper cites [de— re] constructing vlms’ reasoning in counting.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions [de— re] constructing vlms’ reasoning in counting

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:11.255787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a26fea90-62e4-48cb-9b64-df5d58b29896 · outbound

This paper cites Countgd: Multi-modal open-world counting.Advances in Neural Information Processing Systems, 37:48810–48837.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Countgd: Multi-modal open-world counting.Advances in Neural Information Processing Systems, 37:48810–48837

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.027611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation d89c2326-4e53-41c0-9c4e-8c0f05130b4b · outbound

This paper cites Mitigating object hallucinations in large vision- language models with assembly of global and local attention.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Mitigating object hallucinations in large vision- language models with assembly of global and local attention

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:04.990981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation f73601dd-ed6a-42dd-a0b8-1a1d3ba39c4b · outbound

This paper cites Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:04.985016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9774e28b-d991-44de-a42e-74e0be0cce95 · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.022671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6212dcc5-5390-4bb2-8213-47a59f953785 · outbound

This paper cites Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:11.270170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation a9ade269-d54a-4e96-8ffc-b43909c3e09f · outbound

This paper cites Google deepmind: Gemini 2.5 pro, 2025.https: //deepmind.google/models/gemini/pro/.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Google deepmind: Gemini 2.5 pro, 2025.https: //deepmind.google/models/gemini/pro/

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.018425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 47c2a054-1f45-4bb9-8f8b-b7dea0792179 · outbound

This paper cites Your vision-language model can’t even count to 20: Exposing the failures of vlms in compositional counting.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Your vision-language model can’t even count to 20: Exposing the failures of vlms in compositional counting

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:11.274767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 52fd7b0c-2cf3-4531-8914-32d5e35dfeac · outbound

This paper cites Mask r-cnn.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Mask r-cnn

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.024834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bb536c98-020c-4ac6-8c8b-7f2f360deeef · outbound

This paper cites Do Vision-Language Models Really Understand Visual Language?.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Do Vision-Language Models Really Understand Visual Language?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:11.284722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ad1050a2-488b-44a8-b789-af7e8bdb90ba · outbound

This paper cites Point segment and count: A gener- alized framework for object counting.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Point segment and count: A gener- alized framework for object counting

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.030162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 36680381-e702-4798-bef3-21fc87835478 · outbound

This paper cites See What You Are Told: Visual Attention Sink in Large Multimodal Models.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions See What You Are Told: Visual Attention Sink in Large Multimodal Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:11.265815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 43db3c0b-0df8-4f4d-8b58-bd6c4fc60dd3 · outbound

This paper cites Segment any- thing.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Segment any- thing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.032339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 83f6e0c4-13b4-418b-8c0a-1db9950c9f0a · outbound

This paper cites VLind-Bench: Measuring Language Priors in Large Vision-Language Models.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions VLind-Bench: Measuring Language Priors in Large Vision-Language Models

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T20:10:11.260463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bbff3afe-903e-45b8-8e47-1062697a8739 · outbound

This paper cites Vhelm: A holistic evaluation of vision language models.Advances in Neural Information Processing Systems, 37:140632–140666.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Vhelm: A holistic evaluation of vision language models.Advances in Neural Information Processing Systems, 37:140632–140666

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.009905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 624ba155-1d3c-4e23-86fc-9935c6a4592a · outbound

This paper cites Open ai: Introducing openai o3 and o4-mini.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Open ai: Introducing openai o3 and o4-mini

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.011895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 2735e050-9190-458d-81eb-784192dcac4b · outbound

This paper cites Crowd- diff: Multi-hypothesis crowd density estimation using dif- fusion models.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Crowd- diff: Multi-hypothesis crowd density estimation using dif- fusion models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.014360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ad766355-5474-4800-a485-569676b55038 · outbound

This paper cites Vision-language foundation models for medical imag- ing: a review of current practices and innovations.Biomedi- cal Engineering Letters, pages 1–22.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Vision-language foundation models for medical imag- ing: a review of current practices and innovations.Biomedi- cal Engineering Letters, pages 1–22

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.014782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 21312160-65b6-4ecf-b4af-057621342db8 · outbound

This paper cites Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:28:16.822386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 581acd19-16b8-47c0-9fa5-044bef8a5b6f · outbound

This paper cites an unresolved cited work.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-05-17T20:12:05.016160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation ae249483-3f8b-4838-b9e5-4756a27e24e8 · outbound

This paper cites Qwen2.5-vl.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Qwen2.5-vl

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.020643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 1b0deaa2-9c69-41e9-a477-ddc9084260cf · outbound

This paper cites Vi- sion language models are biased.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Vi- sion language models are biased

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.003503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 3e68b964-198f-4516-835f-e2207cadee5a · outbound

This paper cites an unresolved cited work.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-05-17T20:12:04.995735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 29b077c0-1770-4c3a-9da3-ccd5da4963ab · outbound

This paper cites an unresolved cited work.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-05-17T20:12:05.004377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 5a9901a5-6a9c-4cd3-a0f8-a11703245514 · outbound

This paper cites Count the number of objects in this image. Answer the count within curly brackets, eg.{10}.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Count the number of objects in this image. Answer the count within curly brackets, eg.{10}

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.002187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 75b6cfe4-14b3-4cc8-9b37-0f85002bd795 · outbound

This paper cites Example images for theObjectcategory,Colorpattern, showing different object colors.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Example images for theObjectcategory,Colorpattern, showing different object colors

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.034554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation e33c48ad-429c-4133-b0ed-cd128b19bb77 · outbound

This paper cites circles”(as default in color experiment), “squares.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions circles”(as default in color experiment), “squares

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:04.991241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 6740fb6f-adcd-487c-8d1c-78cba9722958 · outbound

This paper cites an unresolved cited work.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-17T20:12:05.007379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 24a5578e-36e6-437c-8cfd-45242978a334 · outbound

This paper cites blue-green.

Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions blue-green

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T20:12:05.005528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Pith citing papers

Observation e88ce197-84e4-4fd7-9f42-1bee320535ed · inbound

PushupBench: Your VLM is not good at counting pushups cites this paper.

PushupBench: Your VLM is not good at counting pushups Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-11T20:36:08.900436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation bd968b84-02ec-406f-8382-f5a9d0d181df · inbound

CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models cites this paper.

CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models Can Vision-Language Models Count? A Synthetic Benchmark and Analysis of Attention-Based Interventions

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:23:17.005033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.