Paper Citation Record · LEDGER

Vision-Language Models Do Not Understand Negation

As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 10 inbound Pith citation observations for arXiv:2501.09425.

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

pith.paper-citation-record.v1
2501.09425 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

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measured 67 of 67 standing notices

One-hop event checks from named stored sources.

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measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:08:58.575726Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T00:18:29.517494Z

Reference resolution

57 of 57 outbound references displayed

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verified fuzzy44
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External citation measurements

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Outbound references

Observation 876019cf-4e11-484d-93b7-b82286f09e37 · outbound

This paper cites Aug- mented reality meets computer vision: Efficient data gen- eration for urban driving scenes.

Vision-Language Models Do Not Understand Negation Aug- mented reality meets computer vision: Efficient data gen- eration for urban driving scenes

Reference 1

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This paper cites Effective conditioned and composed im- age retrieval combining clip-based features.

Vision-Language Models Do Not Understand Negation Effective conditioned and composed im- age retrieval combining clip-based features

Reference 2

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This paper cites FitCLIP: Refining large- scale pretrained image-text models for zero-shot video un- derstanding tasks.

Vision-Language Models Do Not Understand Negation FitCLIP: Refining large- scale pretrained image-text models for zero-shot video un- derstanding tasks

Reference 3

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This paper cites Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Vision-Language Models Do Not Understand Negation Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 4

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This paper cites Learning semantic segmentation from synthetic data: A geo- metrically guided input-output adaptation approach.

Vision-Language Models Do Not Understand Negation Learning semantic segmentation from synthetic data: A geo- metrically guided input-output adaptation approach

Reference 5

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Observation 3e0cc5c6-38ab-4ba6-855b-8a80c4030f3d · outbound

This paper cites The Llama 3 Herd of Models.

Vision-Language Models Do Not Understand Negation The Llama 3 Herd of Models

Reference 6

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This paper cites The pascal visual object classes (voc) challenge.

Vision-Language Models Do Not Understand Negation The pascal visual object classes (voc) challenge

Reference 7

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This paper cites Multimodal Autoregressive Pre-training of Large Vision Encoders.

Vision-Language Models Do Not Understand Negation Multimodal Autoregressive Pre-training of Large Vision Encoders

Reference 8

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This paper cites Datacomp: In search of the next generation of multimodal datasets.

Vision-Language Models Do Not Understand Negation Datacomp: In search of the next generation of multimodal datasets

Reference 9

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This paper cites This is not a dataset: A large negation benchmark to challenge large language mod- els.

Vision-Language Models Do Not Understand Negation This is not a dataset: A large negation benchmark to challenge large language mod- els

Reference 10

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This paper cites Shortcut learning in deep neural networks.

Vision-Language Models Do Not Understand Negation Shortcut learning in deep neural networks

Reference 11

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This paper cites SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?.

Vision-Language Models Do Not Understand Negation SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

Reference 12

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Vision-Language Models Do Not Understand Negation Unresolved cited work

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This paper cites Quilt-1m: One million image-text pairs for histopathology.

Vision-Language Models Do Not Understand Negation Quilt-1m: One million image-text pairs for histopathology

Reference 14

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This paper cites Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison.

Vision-Language Models Do Not Understand Negation Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

Reference 15

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This paper cites Generative models as a data source for multiview representa- tion learning.

Vision-Language Models Do Not Understand Negation Generative models as a data source for multiview representa- tion learning

Reference 16

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Observation 6825bbb5-161f-4b27-8c15-85c4acb02932 · outbound

This paper cites The power of negation in english: Text, context and relevance.

Vision-Language Models Do Not Understand Negation The power of negation in english: Text, context and relevance

Reference 17

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This paper cites Negation in syntax–on the na- ture of functional categories and projections.

Vision-Language Models Do Not Understand Negation Negation in syntax–on the na- ture of functional categories and projections

Reference 18

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This paper cites Naturalbench: Evalu- ating vision-language models on natural adversarial samples.

Vision-Language Models Do Not Understand Negation Naturalbench: Evalu- ating vision-language models on natural adversarial samples

Reference 19

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This paper cites Compre- hending and ordering semantics for image captioning.

Vision-Language Models Do Not Understand Negation Compre- hending and ordering semantics for image captioning

Reference 20

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This paper cites Cross-modal retrieval and semantic re- finement for remote sensing image captioning.

Vision-Language Models Do Not Understand Negation Cross-modal retrieval and semantic re- finement for remote sensing image captioning

Reference 21

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This paper cites Microsoft coco: Common objects in context.

Vision-Language Models Do Not Understand Negation Microsoft coco: Common objects in context

Reference 22

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This paper cites A visual- language foundation model for computational pathology.

Vision-Language Models Do Not Understand Negation A visual- language foundation model for computational pathology

Reference 23

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This paper cites CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval.

Vision-Language Models Do Not Understand Negation CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

Reference 24

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Vision-Language Models Do Not Understand Negation Fine-tuning llama for multi-stage text retrieval

Reference 25

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This paper cites Crepe: Can vision-language foundation models reason compositionally? In CVPR, 2023.

Vision-Language Models Do Not Understand Negation Crepe: Can vision-language foundation models reason compositionally? In CVPR, 2023

Reference 26

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This paper cites Simple open-vocabulary object detection with vi- sion transformers.

Vision-Language Models Do Not Understand Negation Simple open-vocabulary object detection with vi- sion transformers

Reference 27

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This paper cites Recent advances in processing negation.

Vision-Language Models Do Not Understand Negation Recent advances in processing negation

Reference 28

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This paper cites Effect of negation in sentences on sentiment analy- sis and polarity detection.

Vision-Language Models Do Not Understand Negation Effect of negation in sentences on sentiment analy- sis and polarity detection

Reference 29

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Vision-Language Models Do Not Understand Negation Clip-it! language-guided video summarization

Reference 30

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Vision-Language Models Do Not Understand Negation Multi-Stage Document Ranking with BERT

Reference 31

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This paper cites Synthesize diagnose and optimize: Towards fine- grained vision-language understanding.

Vision-Language Models Do Not Understand Negation Synthesize diagnose and optimize: Towards fine- grained vision-language understanding

Reference 32

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This paper cites On guiding vi- sual attention with language specification.

Vision-Language Models Do Not Understand Negation On guiding vi- sual attention with language specification

Reference 33

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Vision-Language Models Do Not Understand Negation Learn- ing transferable visual models from natural language super- vision

Reference 34

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Vision-Language Models Do Not Understand Negation Denseclip: Language-guided dense prediction with context- aware prompting

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Vision-Language Models Do Not Understand Negation Sentence-bert: Sentence embeddings using siamese bert-networks

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Vision-Language Models Do Not Understand Negation High-resolution image syn- thesis with latent diffusion models

Reference 37

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Vision-Language Models Do Not Understand Negation Clip for all things zero-shot sketch-based image retrieval, fine- grained or not

Reference 38

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This paper cites LAION-5b: An open large-scale dataset for train- ing next generation image-text models.

Vision-Language Models Do Not Understand Negation LAION-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 39

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Vision-Language Models Do Not Understand Negation How much can clip benefit vision-and-language tasks? In International Conference on Learning Representa- tions

Reference 40

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Vision-Language Models Do Not Understand Negation Proposalclip: Unsupervised open-category object pro- posal generation via exploiting clip cues

Reference 41

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Vision-Language Models Do Not Understand Negation Cliport: What and where pathways for robotic manipulation

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This paper cites Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations.

Vision-Language Models Do Not Understand Negation Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations

Reference 43

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Vision-Language Models Do Not Understand Negation Stablerep: Synthetic images from text-to- image models make strong visual representation learners

Reference 44

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Vision-Language Models Do Not Understand Negation Learning vision from mod- els rivals learning vision from data

Reference 45

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This paper cites Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning.

Vision-Language Models Do Not Understand Negation Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning

Reference 46

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This paper cites Language models are not naysayers: an anal- ysis of language models on negation benchmarks.

Vision-Language Models Do Not Understand Negation Language models are not naysayers: an anal- ysis of language models on negation benchmarks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:08:46.635529Z

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Observation 57d0e0a7-a39f-419a-8995-ab824dc0e9ca · outbound

This paper cites Msr-vtt: A large video description dataset for bridging video and language.

Vision-Language Models Do Not Understand Negation Msr-vtt: A large video description dataset for bridging video and language

Reference 48

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This paper cites Real-fake: Effective training data synthesis through distribution matching.

Vision-Language Models Do Not Understand Negation Real-fake: Effective training data synthesis through distribution matching

Reference 49

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Observation 28592488-7af3-4134-a2ea-ce92e7c9575c · outbound

This paper cites When and why vision- language models behave like bags-of-words, and what to do about it? In ICLR, 2023.

Vision-Language Models Do Not Understand Negation When and why vision- language models behave like bags-of-words, and what to do about it? In ICLR, 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:08:46.588431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56c7ca48-e364-4fab-afa8-8f56e3500ae9 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

Vision-Language Models Do Not Understand Negation Lit: Zero-shot transfer with locked-image text tuning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:08:46.569731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3b85b920-c49b-45cb-b1c9-3d6d2ee36ec1 · outbound

This paper cites Sigmoid loss for language image pre-training.

Vision-Language Models Do Not Understand Negation Sigmoid loss for language image pre-training

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:08:46.552358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5bdf1839-48ec-41c2-8e4d-f6c92c722db5 · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Vision-Language Models Do Not Understand Negation BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T20:08:46.310828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31792e35-f68e-4868-919a-52efb1ade9b3 · outbound

This paper cites an unresolved cited work.

Vision-Language Models Do Not Understand Negation Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:08:46.517398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eebb94a9-a692-433c-88eb-a37f3f06fbda · outbound

This paper cites Yes.” over “No.

Vision-Language Models Do Not Understand Negation Yes.” over “No

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:08:46.500166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6ff600fa-2015-4ed7-996a-b3c7c3a277ae · outbound

This paper cites an unresolved cited work.

Vision-Language Models Do Not Understand Negation Unresolved cited work

Reference 2022

Resolution: parse uncertain; raw_fallback, observed 2026-08-10T20:08:47.498368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1367e721-a6e2-480a-80d9-672a22dd9629 · outbound

This paper cites an unresolved cited work.

Vision-Language Models Do Not Understand Negation Unresolved cited work

Reference 2023

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T20:08:46.534065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 4139ed13-aaf4-457f-826e-c0a487ba724c · inbound

On the Compatibility of Generative AI and Generative Linguistics cites this paper.

On the Compatibility of Generative AI and Generative Linguistics Vision-Language Models Do Not Understand Negation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T19:40:04.817443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a51e034-03ea-4726-a7a1-89714e6234a0 · inbound

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP cites this paper.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Vision-Language Models Do Not Understand Negation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:58.344234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93ca8fa6-82d7-43c1-853c-34dbd8976c31 · inbound

NegVQA: Can Vision Language Models Understand Negation? cites this paper.

NegVQA: Can Vision Language Models Understand Negation? Vision-Language Models Do Not Understand Negation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:09.271049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08c993ae-bb63-403f-bdf3-428abeaada82 · inbound

Negation-Aware Test-Time Adaptation for Vision-Language Models cites this paper.

Negation-Aware Test-Time Adaptation for Vision-Language Models Vision-Language Models Do Not Understand Negation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:08:58.575726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af5a841d-b63e-4378-9185-5f06f40dddbf · inbound

Decomposing Visual Classification: Assessing Tree-Based Reasoning in VLMs cites this paper.

Decomposing Visual Classification: Assessing Tree-Based Reasoning in VLMs Vision-Language Models Do Not Understand Negation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T20:28:21.003431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65823167-c3b5-467e-9557-9740e1845312 · inbound

Disparities In Negation Understanding Across Languages In Vision-Language Models cites this paper.

Disparities In Negation Understanding Across Languages In Vision-Language Models Vision-Language Models Do Not Understand Negation

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:31:03.990536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8f28e7c4-ea6d-489f-935b-6c6cc2a6296d · inbound

Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface cites this paper.

Trace Mutation in Human-LLM Dialogue: The Transcript as Forensic and Mitigation Surface Vision-Language Models Do Not Understand Negation

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-14T00:18:29.524884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2471455d-3f34-45b8-8239-b49ecff50ba2 · inbound

CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs cites this paper.

CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs Vision-Language Models Do Not Understand Negation

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:41:09.727332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 10e8c895-0807-4b6d-a917-130295408816 · inbound

Uneven Evolution of Cognition Across Generations of Generative AI Models cites this paper.

Uneven Evolution of Cognition Across Generations of Generative AI Models Vision-Language Models Do Not Understand Negation

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:50:58.108557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 64c36674-3a95-4912-9f7c-3014d8fc0d3a · inbound

Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution cites this paper.

Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution Vision-Language Models Do Not Understand Negation

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T17:16:49.314099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.