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Paper Citation Record · LEDGER

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2505.18434.

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

pith.paper-citation-record.v1
2505.18434 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:35:00.461950Z

measured 41 of 41 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T19:15:11.575505Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T19:16:31.450717Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c9d0cb42-2f23-4ea5-9148-2962863cff81 · outbound

This paper cites online" 'onlinestring :=.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP online" 'onlinestring :=

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:34:57.896668Z digest=sha256:ef5267dcc52795443ed8f08bb3bdbf8c900da54fd01cebc46a84b0f265ea42df

Observation 306b044b-2668-49fe-aaf1-d7081cfcad73 · outbound

This paper cites write newline.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP write newline

Reference 2

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source=arxiv_source observed=2026-08-07T14:34:58.031815Z digest=sha256:8d200f2327ce214988baf4f8bb9d9f1dc099e41266d459ce62135e0c9abef8b4

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

This paper cites Vision-Language Models Do Not Understand Negation.

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

Reference 4

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source=arxiv_source observed=2026-08-07T14:34:58.344234Z digest=sha256:fb28a848660a6ce98d187ce869ae0a73d1f93761890fc58148f77354470b16a1

Observation 2b108680-dfdc-46d3-b705-51d38cde0f40 · outbound

This paper cites O'Reilly Media, Inc.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP O'Reilly Media, Inc

Reference 5

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source=arxiv_source observed=2026-08-07T14:34:58.468407Z digest=sha256:32e05a7526be2b4d43ce2f9f3295756ce8de5d2e89a59586395cd6457d09cc1e

Observation d2b9491a-266b-4ec7-bef6-c6006df35f50 · outbound

This paper cites Language Models are Few-Shot Learners.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Language Models are Few-Shot Learners

Reference 6

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source=arxiv_source observed=2026-08-07T14:34:58.621742Z digest=sha256:b2a9d567eda85a610dd6e089c685579c77cb24850a60cbda2cfaae1638e4cf02

Observation e79b67e6-d8cc-4ebe-b975-5d3dea00e77c · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:34:58.797807Z digest=sha256:90d61a124a4b099494a11f3deab644be4371cc2610f92092190a5bacf3728553

Observation d6a4368f-86b2-4cff-ba4a-aed2cc1eaf71 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 8

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raw_fallback, observed 2026-08-07T14:35:01.388403Z

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.

source=arxiv_source observed=2026-08-07T14:34:58.904318Z digest=sha256:ce3519159968c1c12149dd19b0c6f5fda6f31fec42ebfb93b6f67869058fc188

Observation 24bc5e7d-237e-4aea-a8b8-1b81580e35b4 · outbound

This paper cites Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

Reference 9

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source=arxiv_source observed=2026-08-07T14:34:59.063835Z digest=sha256:058d9786938be904e9b1b59150d8ca0333af0ba2226aed7b4c8ff33c51c3db82

Observation 96eaa59d-1ad0-4f11-bbc3-5b58e322ed64 · outbound

This paper cites Impact of Noisy Supervision in Foundation Model Learning.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Impact of Noisy Supervision in Foundation Model Learning

Reference 10

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source=arxiv_source observed=2026-08-07T14:34:59.229920Z digest=sha256:f54ac9fa3681f50d1d87d4ef10adb90e1234408bff7e22c5699b6d49a8e5e59f

Observation ed928f12-0e0e-4068-9a70-60c50677747e · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:34:59.356659Z digest=sha256:fd9a99375c13a3b8b1afb7bcc722377f345ce870a1cee2bc748dc7f4ec8efa7e

Observation 4b592db5-4eaa-4624-8c27-6084533a4246 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 12

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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.

source=arxiv_source observed=2026-08-07T14:34:59.511051Z digest=sha256:ecf95be69dda9b9b1c88f17ee5a5e2fd438c523cbeb88827a96af83d5ab3cced

Observation f001e031-08e8-45ac-967e-f2ee853f5eca · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Scaling Instruction-Finetuned Language Models

Reference 13

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source=arxiv_source observed=2026-08-07T14:34:59.627115Z digest=sha256:b19bcc668e595f8792936ec65be1ed869095969210365ab9b4d37c879117d3aa

Observation 2b5ce8d2-4753-4f89-bbac-cb02303aa3f5 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 14

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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.

source=arxiv_source observed=2026-08-07T14:34:59.747994Z digest=sha256:b9b3a24e5b7242c7e054d26afa42836b4675809e8960c992434faa07451ee67e

Observation 47d76b6c-c3b1-46c2-9c1f-99288e3e0672 · outbound

This paper cites Everingham, L.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Everingham, L

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T14:35:01.330130Z

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.

source=arxiv_source observed=2026-08-07T14:34:59.863173Z digest=sha256:a8e4c3d29a2a62ff0d331539d7ed2235195b448b1745ef4aeeb08feadde0976b

Observation 1a41bdd0-012e-4d5d-9687-6606f9dd0f03 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 16

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source=arxiv_source observed=2026-08-07T14:35:00.036352Z digest=sha256:f9e677497f2c7abff50a5872f9d0e0297ffdb3d2eb1c47f85cd7adbc3e2ab21c

Observation dcf6fbc6-a23b-4a18-bb99-9ffac3e87109 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 17

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raw_fallback, observed 2026-08-07T14:35:01.312841Z

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.

source=arxiv_source observed=2026-08-07T14:35:00.179358Z digest=sha256:36dd201e5cee602fa123b08b7bfed6e036928480ab315539df59b5e2a6718652

Observation ecd4db44-fa06-448d-b888-2d51b40f7100 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 18

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T14:35:00.281112Z digest=sha256:52ec506fce6e36a4dc77bf28ba50e25369cb0bb61e4e287ccc17b556869c868b

Observation 338c2a94-95c9-43ff-94cb-622dd6a851e1 · outbound

This paper cites TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering

Reference 19

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source=arxiv_source observed=2026-08-07T14:35:00.351522Z digest=sha256:e330c583a1edcbf36f8c877b96974ba86d2bd43829b289a3bfffc42a157ebfac

Observation d320334e-ef9c-49b0-aac3-8208f8a951de · outbound

This paper cites Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 20

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source=arxiv_source observed=2026-08-07T14:35:00.356911Z digest=sha256:75137edf782759046f8e8f8ae106b8cccfa04316f3d371a7db9fc9b2cad5633d

Observation 8bddf210-a09c-4092-910c-691bb03ca8db · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 21

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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.

source=arxiv_source observed=2026-08-07T14:35:00.361568Z digest=sha256:f349e7f7c82ae5f0dcf02b5584077441ca179a57d7f62187f139d60d350511ee

Observation d3fed293-b268-4ba2-9eff-878f1981a22d · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 22

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source=arxiv_source observed=2026-08-07T14:35:00.366526Z digest=sha256:3b73efb4b4cc18ebe4591745511f403d2449188194a28156f94de1a882438f10

Observation 8aceb938-ba68-4d48-bf61-407c5439ebde · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.371300Z digest=sha256:5dfad80b9bf4081ae729f80cb5f02a692b927b53cc41e608eba0d6658074bdcf

Observation fa7b9d85-8e92-4008-8008-7d8ef8d08a2f · outbound

This paper cites GPT-4o System Card.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP GPT-4o System Card

Reference 24

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source=arxiv_source observed=2026-08-07T14:35:00.376552Z digest=sha256:e5ab033a9ae1acb977316e9550e0e9040500cd3a67db731073059d125ce78444

Observation b2e067bb-131c-47b9-b3db-ca0b521d1a5f · outbound

This paper cites Training language models to follow instructions with human feedback.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Training language models to follow instructions with human feedback

Reference 25

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source=arxiv_source observed=2026-08-07T14:35:00.381255Z digest=sha256:2a219fa00d4a4bab3526a48be071d1277e5f543c95529f616f92ae5989d1b577

Observation 2a9a22b1-ff96-4627-9f31-8f3b6b1c68b1 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.386059Z digest=sha256:798100100f323f3367f20004577f84eab74777781ce081880eb4b5bd9e840576

Observation f12c39f5-396f-4035-bf27-1220bb29279f · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 27

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source=arxiv_source observed=2026-08-07T14:35:00.391230Z digest=sha256:eff06e02f46e40bf547665ba57b953e6a560e0afc44822d96b50c90c871ea976

Observation be44f958-c33a-4fd3-8214-91d47c34f5a9 · outbound

This paper cites Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

Reference 28

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.396259Z digest=sha256:0ef586421b00988c899dd42cc0bb7c496fc6e07f4e4764be2133c0e52f8ab08e

Observation 081d8f11-b579-4847-af2e-bf6959768789 · outbound

This paper cites How and where does CLIP process negation?.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP How and where does CLIP process negation?

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.401122Z digest=sha256:9d74faeb694dbd173a4441489e629af634babf3934df4c412c973dea6f6a506c

Observation 78d32ba2-ff16-40f4-b03b-4182cc671e6b · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Learning Transferable Visual Models From Natural Language Supervision

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.406088Z digest=sha256:be31a67b230ab67d5764668fd5f9b6c250ad228405b67139c91a3610448edd4d

Observation 94964b6f-697f-4780-b5ff-52f8dd83537c · outbound

This paper cites Deep Learning is Robust to Massive Label Noise.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Deep Learning is Robust to Massive Label Noise

Reference 31

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no resolver link, observed 2026-08-07T14:35:00.411297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.411297Z digest=sha256:8a48007d1f251d819cdb7cbc2a0ce883605e30e945e240b3329c19c52678645b

Observation 21b6a9be-a77f-4c24-a7ce-059127703e79 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP High-Resolution Image Synthesis with Latent Diffusion Models

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.417623Z digest=sha256:9614eda8c3d749a2e83dbe4163fd4f4f2ae02c92a8ff9661b65072296c105256

Observation e7af6781-2a22-4476-b321-3d843c4160e8 · outbound

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

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.422103Z digest=sha256:c041ca7dfca0c9aac4b2616924f938b5f0d717c3b534be41b7846a66afdc8b67

Observation 3ad4ee9f-4a6c-485e-aa14-4980c0338733 · outbound

This paper cites Language models are not naysayers: An analysis of language models on negation benchmarks.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Language models are not naysayers: An analysis of language models on negation benchmarks

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.426587Z digest=sha256:16043db8f0ab3c961d4dd3d9e987b81ce5b0617524b869c10b5833cda7f2bde3

Observation a242486e-4317-48d8-ba10-ddb9dd126f08 · outbound

This paper cites an unresolved cited work.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Unresolved cited work

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.433365Z digest=sha256:266dbe45a2b7fd86aa948b2259050c5d38c6a7b72b7a6e64af7bb879bdbf5916

Observation 536df0a5-e6e6-4cc0-ba2f-6f9bd21f55ab · outbound

This paper cites Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation

Reference 36

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no resolver link, observed 2026-08-07T14:35:00.437911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.437911Z digest=sha256:dda78340910b7f916cb070bf5a698ac49f9bb794073edf0a20439b1d80837659

Observation 71c6ea5d-ee54-48e8-9f1c-25d5aa41e6f5 · outbound

This paper cites Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation

Reference 37

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verified exact
local_arxiv, observed 2026-08-07T14:35:00.751105Z

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.

source=arxiv_source observed=2026-08-07T14:35:00.442422Z digest=sha256:3aea1be4b2189f4032e5919b1e53cd2f35c0664e800b0edbcf714828a0e0a092

Observation da38d354-1cf5-4f4f-930b-eb6cb87ca16a · outbound

This paper cites Ehinger, Aude Oliva, and Antonio Torralba.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Ehinger, Aude Oliva, and Antonio Torralba

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:35:00.447280Z digest=sha256:d027f9be3d95b49a87f12ee0cf80fdde8485c1271853577b62654f8201c7e5ea

Observation 873d0639-3ea1-42a6-9779-70aaa38b6784 · outbound

This paper cites Self-training with Noisy Student improves ImageNet classification.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Self-training with Noisy Student improves ImageNet classification

Reference 39

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unresolved
no resolver link, observed 2026-08-07T14:35:00.452635Z

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source=arxiv_source observed=2026-08-07T14:35:00.452635Z digest=sha256:e0239aaef97e2a328921d3a7b8fb422df940b499f10eac5531dfe69eb8ab9310

Observation 3b905e8a-b3f0-4abc-a0ef-56ac6cc61eab · outbound

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

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T14:35:00.457612Z

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source=arxiv_source observed=2026-08-07T14:35:00.457612Z digest=sha256:5c5b04f0ec8379c3f6dc457de9ab7dbaca9af4b96ff952da3c75d0332c2a8662

Observation 63328273-7ae5-40af-986b-50220ad4acde · outbound

This paper cites Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models.

TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T14:35:00.461950Z

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source=arxiv_source observed=2026-08-07T14:35:00.461950Z digest=sha256:2ae0bb7302d7d67b391450cc187a7d708516ef78479b9fa0433d74ed322acda9

Pith citing papers

Observation 5359fe90-338b-42bc-8583-488d140a8ca2 · inbound

Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk? cites this paper.

Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk? TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP

Reference 1

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verified exact
arxiv_id, observed 2026-05-15T19:16:31.454290Z

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source=pdf_text observed=2026-05-15T19:15:11.575505Z digest=sha256:fc7f344a94cfb4d8e1418bbaa0e325a79d849ec471b4c259ffb5126d45314f65