Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:08:46.326666Z
Paper Citation Record · LEDGER
Vision-Language Models Do Not Understand Negation
As of 11 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 8 inbound Pith citation observations for arXiv:2501.09425.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:58.344234Z
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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External citation measurements
No source-named external measurement is stored.
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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Observation 76626a12-b8c4-4540-861e-c24ddca9336b · outbound
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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Observation 5d08414e-b6b6-492a-ac86-dbac81d7a1fe · outbound
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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Observation adc40a9b-4ad3-44b7-96a0-b98ec2b7ff5d · outbound
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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Observation 19db9dd0-2961-4ed7-ad64-5bae5646be84 · outbound
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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Observation 97d27b26-b7c2-45d5-ab5c-dbe351a3529a · outbound
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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Observation 11613278-2c26-4b96-9ae0-c64f4c515b2a · outbound
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
- Resolution
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Source-reported events for the cited work
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Observation 43de77e5-ed52-44ba-8ed4-68d1bedef7ab · outbound
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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Source-reported events for the cited work
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Observation e1117c67-0df2-4e42-a899-fb9d4b5c7bcd · outbound
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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Source-reported events for the cited work
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Observation 504dc068-fbb3-4026-b1d4-87f3c4e9d131 · outbound
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
Reference 13
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Observation fd484441-67ad-4796-b2bf-0cc61cb951c7 · outbound
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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Observation a0594ace-e525-4638-b328-70feab20beaa · outbound
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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Observation b40567b7-2ebc-44d1-a989-c1c016b6515d · outbound
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
- Resolution
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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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Observation 51eb013c-e984-4379-8ec6-8ce2bcc29324 · outbound
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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Observation 9c371f82-ffe9-4c86-b92b-0595593f0e24 · outbound
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
- Resolution
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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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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
Reference 35
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Vision-Language Models Do Not Understand Negation Sentence-bert: Sentence embeddings using siamese bert-networks
Reference 36
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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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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
- Resolution
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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
Reference 42
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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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Vision-Language Models Do Not Understand Negation Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning
Reference 46
- Resolution
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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
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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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Vision-Language Models Do Not Understand Negation Real-fake: Effective training data synthesis through distribution matching
Reference 49
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
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 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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.