Equally Critical: Samples, Targets, and Their Mappings in Datasets | Paper Citation Record

Observation 13e6047b-56a6-4688-942b-64f9c4df197f · outbound

This paper cites Language models are few-shot learners.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Language models are few-shot learners

Reference 1

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Observation c57b2196-4d00-4f6a-aa1c-ea2199dddf85 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Emerging properties in self-supervised vision transformers

Reference 2

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Observation 0ea1fd40-7260-400d-a668-2dcb220b3d03 · outbound

This paper cites Semi- supervised knowledge distillation for model compression.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Semi- supervised knowledge distillation for model compression

Reference 3

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Observation 8ae60da6-ecba-4438-88cd-ba7ae77624a5 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Equally Critical: Samples, Targets, and Their Mappings in Datasets A simple framework for contrastive learning of visual representations

Reference 4

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Observation c81c9ffe-bd02-4f55-9c44-26e64e0b6a89 · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Equally Critical: Samples, Targets, and Their Mappings in Datasets An empirical study of training self-supervised vision transformers

Reference 5

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Observation ba9c1090-111d-416b-94f4-b82b7664c2ff · outbound

This paper cites A Survey of Model Compression and Acceleration for Deep Neural Networks.

Equally Critical: Samples, Targets, and Their Mappings in Datasets A Survey of Model Compression and Acceleration for Deep Neural Networks

Reference 6

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Observation 1fe0ebc7-cc34-40a1-a7ff-0e15688c40cd · outbound

This paper cites Robust locally weighted regression and smoothing scatterplots.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Robust locally weighted regression and smoothing scatterplots

Reference 7

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Observation 2f2ce208-b557-480d-bb01-34a2eb91b132 · outbound

This paper cites Dc-bench: Dataset condensation bench- mark.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Dc-bench: Dataset condensation bench- mark

Reference 8

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Observation dfb2c368-3083-4e0c-bdec-c949b6d12704 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Imagenet: A large- scale hierarchical image database

Reference 9

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Observation a6540a8e-d526-47ae-ac8e-615b80c8f543 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Imagenet: A large- scale hierarchical image database

Reference 10

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Observation 0726f5b4-f70c-44b2-862d-f6c781000aae · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Equally Critical: Samples, Targets, and Their Mappings in Datasets An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

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Observation 89726db7-ddff-474d-a191-56d89987e9e9 · outbound

This paper cites Scaling laws of synthetic images for model training.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Scaling laws of synthetic images for model training

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.597753Z

Source-reported events for the cited work

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Observation 03f07747-c3a0-44d1-a209-531a759197e5 · outbound

This paper cites How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization.

Equally Critical: Samples, Targets, and Their Mappings in Datasets How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.426143Z

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Observation fc7ca5e1-fc27-4081-b86f-cbd61835be47 · outbound

This paper cites Knowledge distillation: A survey.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Knowledge distillation: A survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.431570Z

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Observation 0ec3d483-4528-4b40-88c6-70e20e8c26e2 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Bootstrap your own latent-a new approach to self-supervised learning

Reference 15

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Observation fa4bc89a-0884-481e-93b2-3867bf232598 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

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Observation ed595c9a-71af-4173-a71c-e80e41e88af1 · outbound

This paper cites To- wards lossless dataset distillation via difficulty-aligned trajectory matching.

Equally Critical: Samples, Targets, and Their Mappings in Datasets To- wards lossless dataset distillation via difficulty-aligned trajectory matching

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.561286Z

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Observation 60299f54-7b49-4f07-acd2-65b92795bc1f · outbound

This paper cites Clip and complementary methods.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Clip and complementary methods

Reference 18

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Observation d279a424-5265-47b0-bc4d-0fade30ff693 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Momentum contrast for unsupervised visual representation learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.456452Z

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Observation 471dae49-be9c-4d60-9d01-058e44a8856f · outbound

This paper cites Deep residual learning for im- age recognition.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Deep residual learning for im- age recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.516888Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 48a323c5-b6a4-4a58-93f5-37a136613d66 · outbound

This paper cites Knowledge adaptation: Teaching to adapt.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Knowledge adaptation: Teaching to adapt

Reference 21

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Observation bc362be3-2c1d-4261-818e-076d4d1052ee · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Deep Learning Scaling is Predictable, Empirically

Reference 22

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Observation c4ebba1f-1dee-4848-adc7-737ce8bf9626 · outbound

This paper cites Distilling the knowledge in a neural network.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Distilling the knowledge in a neural network

Reference 23

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Observation 477a86c3-3b38-40c8-90a2-33cf8e408838 · outbound

This paper cites Densely connected convolutional networks.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Densely connected convolutional networks

Reference 24

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Observation 885d8c1a-4b59-4361-9a60-749f6556dbbc · outbound

This paper cites Scaling Laws for Neural Language Models.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Scaling Laws for Neural Language Models

Reference 25

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Observation c0b73dd0-59e7-4488-8f20-15fc62d2a6c9 · outbound

This paper cites Supervised contrastive learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Supervised contrastive learning

Reference 26

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Observation e55e2dac-d8df-4f53-bc5d-af971cbe6dda · outbound

This paper cites Learning multiple layers of features from tiny images.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Learning multiple layers of features from tiny images

Reference 27

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Observation c0ea027b-919e-44fc-960c-692714c800bb · outbound

This paper cites Tiny imagenet visual recognition challenge.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Tiny imagenet visual recognition challenge

Reference 28

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Observation e8efec1f-10c5-4d9d-907b-2a18c57abbdb · outbound

This paper cites A survey on text classification: From traditional to deep learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets A survey on text classification: From traditional to deep learning

Reference 29

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Observation 14b24bbf-63d8-420b-9f21-fcd223677268 · outbound

This paper cites Image segmentation using deep learning: A survey.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Image segmentation using deep learning: A survey

Reference 30

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Observation 5c363c79-8e0c-46ce-9d00-28437fc15e39 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Deep learning on a data diet: Finding important examples early in training

Reference 31

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Observation 5fdf8e5a-78ce-46dc-a59f-d65acf0aca60 · outbound

This paper cites The Effectiveness of Data Augmentation in Image Classification using Deep Learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Reference 32

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Observation dbf4e0a7-4eae-464e-b788-d6fac9b36e02 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Learning transferable visual models from natural language supervision

Reference 33

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Observation 0f1f3e6e-da3f-402b-9a6d-7de64d7dbd41 · outbound

This paper cites Data efficient learning of molecular slow modes from nonequi- librium metadynamics.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Data efficient learning of molecular slow modes from nonequi- librium metadynamics

Reference 34

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Observation 2539b64a-3769-4a45-971b-32b125960912 · outbound

This paper cites Deep clustering: A comprehensive survey.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Deep clustering: A comprehensive survey

Reference 35

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Observation 12fbbf47-6f93-4c9f-ad52-44951f6811b0 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 36

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Source-reported events for the cited work

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Observation 6efae488-b391-4265-ae20-489bfbecd0be · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Equally Critical: Samples, Targets, and Their Mappings in Datasets DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 37

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Observation 7e15430b-1780-4a56-a1b9-d76522afec50 · outbound

This paper cites A survey on image data augmentation for deep learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets A survey on image data augmentation for deep learning

Reference 38

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Observation 64d05157-4a98-4041-8202-33488d5c6514 · outbound

This paper cites Beyond neu- ral scaling laws: beating power law scaling via data pruning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Beyond neu- ral scaling laws: beating power law scaling via data pruning

Reference 39

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Observation 9ee67909-a833-4e30-885a-e6d42b86f222 · outbound

This paper cites Efficiency for Free: Ideal Data Are Transportable Representations.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Efficiency for Free: Ideal Data Are Transportable Representations

Reference 40

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Observation 3f911e52-bfb5-463c-b1a3-ce0bda09c8eb · outbound

This paper cites On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm.

Equally Critical: Samples, Targets, and Their Mappings in Datasets On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm

Reference 41

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 54fa81f7-1c0a-4ab2-8e66-c04da15c4955 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 42

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Observation 3da652a7-af25-420a-99f8-ee92cb770706 · outbound

This paper cites Cafe: Learning to condense dataset by aligning features.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Cafe: Learning to condense dataset by aligning features

Reference 43

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Observation 13c24f4a-3444-42f5-a691-5939cd16671d · outbound

This paper cites Knowledge Distillation Meets Self-Supervision.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Knowledge Distillation Meets Self-Supervision

Reference 44

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Observation 95ef7610-fde2-40eb-8d78-2c3946d41dc3 · outbound

This paper cites A gift from knowledge distillation: Fast optimization, network minimization and transfer learning.

Equally Critical: Samples, Targets, and Their Mappings in Datasets A gift from knowledge distillation: Fast optimization, network minimization and transfer learning

Reference 45

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 025cd46b-a911-4593-abbd-17ae28e4abd7 · outbound

This paper cites Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

Reference 46

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Source-reported events for the cited work

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Observation bf89ba15-6088-4511-8bf9-abfef1038b3c · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.249842Z

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Observation 3b2036f6-36b1-4a1f-948e-9a4b8d2d93a3 · outbound

This paper cites Barlow twins: Self- supervised learning via redundancy reduction.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Barlow twins: Self- supervised learning via redundancy reduction

Reference 48

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Source-reported events for the cited work

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Observation 74943854-ba0b-4b8e-8ee6-b88d57ed5ce0 · outbound

This paper cites Network representation learning: A survey.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Network representation learning: A survey

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.223782Z

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Observation 30aa4347-bcea-4884-b43c-3b90d3f0d446 · outbound

This paper cites Mixup: Beyond empirical risk minimization.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Mixup: Beyond empirical risk minimization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.207703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation afccdc3a-6a35-444f-8d4a-f8e2a35b87cf · outbound

This paper cites Dataset condensation with distribution matching.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Dataset condensation with distribution matching

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.608983Z

Source-reported events for the cited work

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Observation 37a96e0a-d4ec-498c-8354-78d59661f86a · outbound

This paper cites Dataset Condensation with Gradient Matching.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Dataset Condensation with Gradient Matching

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.614054Z

Source-reported events for the cited work

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Observation b71e3333-0849-4a32-bd5d-ea0f0f49186c · outbound

This paper cites Decoupled knowledge distillation.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Decoupled knowledge distillation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.618781Z

Source-reported events for the cited work

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Observation 9bd09116-6ce5-437b-858b-14b913b715ea · outbound

This paper cites Data-efficient learning with active label cleaning and dynamic curriculum.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Data-efficient learning with active label cleaning and dynamic curriculum

Reference 54

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:50:33.171994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1a0a1d6a-02ed-4a54-8d7d-56ef9645951f · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.157034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57418720-6037-4a2a-96a0-ec83753af824 · outbound

This paper cites Limitations.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Limitations

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.142245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation edeee564-a86b-43fe-8c80-5fcefdbb50a8 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.126027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1b5b25be-8b3d-4d5d-9af9-9946bd5ab1c5 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not include experiments

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.110299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b3c6332-81f5-40dd-b7cd-7b80507d1c25 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.094880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ecd2a3ef-3b8e-4d4a-8fe1-bb6319fc63e1 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not include experiments

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.078288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6895b128-f5ea-4de2-bf11-6b2556367532 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not include experiments

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.062942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74f39faa-0d49-45a1-90f7-04692672558e · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not include experiments

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.045517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0290223e-c8c7-415f-b81a-9cf541e443d3 · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.028467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52ab232a-9a12-4f8f-8741-98a15b4c5402 · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:33.012959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52b2ba1d-0edd-47ff-b1c2-9d5ee7205de2 · outbound

This paper cites • The answer NA means that the paper poses no such risks.

Equally Critical: Samples, Targets, and Their Mappings in Datasets • The answer NA means that the paper poses no such risks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:32.996405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 84e8eba8-2533-4b7f-b441-a066093b5242 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not use existing assets

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:32.980788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bab6aec4-02ba-478b-a1b1-dcc37e4e1803 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not release new assets

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:32.963721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05e40eaf-c514-46ca-b4be-62900055a553 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.694065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b03837dc-80b5-4e43-bde4-e1b4c3448ba6 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T20:50:32.699690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1800a3e6-8015-41fe-9863-4c32ee3c2ca1 · outbound

This paper cites Answer: [NA] 29 Justification: LLM is used only for editing.

Equally Critical: Samples, Targets, and Their Mappings in Datasets Answer: [NA] 29 Justification: LLM is used only for editing

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:50:32.928195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers