Paper Citation Record · LEDGER

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2507.20453.

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

pith.paper-citation-record.v1
2507.20453 v3

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:39:11.853587Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:22:14.762729Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy7
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5870c6bf-742b-4fee-a443-1a95d21e4d52 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736, 2022.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736, 2022

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.441503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22a9e9b6-89f2-4f9b-8eba-bfb12d1bd8e1 · outbound

This paper cites Understanding robustness of transformers for image classification.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Understanding robustness of transformers for image classification

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:14.965696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0f3f48ad-df09-4c95-a872-7c1e59b22d4a · outbound

This paper cites End-to-end object detection with transformers.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations End-to-end object detection with transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.600611Z

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Observation 2882d72b-abbe-4bdc-a7db-d554fc2279aa · outbound

This paper cites Rethinking Attention with Performers.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Rethinking Attention with Performers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.725621Z

Source-reported events for the cited work

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Observation 9bd29eca-f9cb-4cc2-b7cb-120d481ac684 · outbound

This paper cites Autoaugment: Learning augmentation strategies from data.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Autoaugment: Learning augmentation strategies from data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.839052Z

Source-reported events for the cited work

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Observation 4c832bd2-53c7-456c-85e3-c39bd04f6224 · outbound

This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:07.904746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7edc3bca-a5e1-4e6b-8ae8-2835fddf658d · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Improved Regularization of Convolutional Neural Networks with Cutout

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.033556Z

Source-reported events for the cited work

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Observation 3b24ecae-da8e-44fa-a01d-3f9abed2507c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.207466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e16ae978-1952-4651-ac2e-7c5851014a6d · outbound

This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.350999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8517328c-d79c-4fe0-ab3a-4c568edbdbc9 · outbound

This paper cites AST: Audio Spectrogram Transformer.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations AST: Audio Spectrogram Transformer

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.440360Z

Source-reported events for the cited work

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Observation 7fd47f6c-1443-4dce-9fc5-14005e8b750a · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Explaining and Harnessing Adversarial Examples

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.572368Z

Source-reported events for the cited work

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Observation a5d92a85-4446-453a-99f4-beb937c84657 · outbound

This paper cites Deep residual learning for image recognition.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Deep residual learning for image recognition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.649221Z

Source-reported events for the cited work

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Observation 40677a84-cce1-4580-b2ee-df15b1dbe1af · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.726255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e78be30-efab-4974-89d9-c45413ca3e58 · outbound

This paper cites Imagenette: A smaller subset of 10 easily classified classes from imagenet.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Imagenette: A smaller subset of 10 easily classified classes from imagenet

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:14.677106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 477624a4-e0b4-47ea-bfb1-3fe4620f2a6f · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:08.967669Z

Source-reported events for the cited work

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Observation 506b581b-65f8-401f-85ac-ef834d2e0caa · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:09.045191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846dee64-4276-434e-a023-b7f9698855f2 · outbound

This paper cites Vilt: Vision-and-language transformer without convolution or region supervision.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Vilt: Vision-and-language transformer without convolution or region supervision

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:09.169731Z

Source-reported events for the cited work

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Observation 160786ee-16c0-452e-a040-65e79d35b843 · outbound

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

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Learning multiple layers of features from tiny images

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:09.255703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a45f5797-fc5a-4f97-b563-d9f4efdff127 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Imagenet classification with deep convolutional neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:14.323587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d2290f4a-af96-4315-b66f-db674b25f533 · outbound

This paper cites Doubly stochastic normalization of the gaussian kernel is robust to heteroskedastic noise.SIAM journal on mathematics of data science, 3(1):388–413, 2021.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Doubly stochastic normalization of the gaussian kernel is robust to heteroskedastic noise.SIAM journal on mathematics of data science, 3(1):388–413, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:14.091118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d800f262-9164-4660-9d08-d8369c09c8e5 · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:09.566945Z

Source-reported events for the cited work

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Observation d842f138-7091-43e1-b584-b1662f41d283 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Swin transformer: Hierarchical vision transformer using shifted windows

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:09.646704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c878e9e-a404-448d-8fe3-7ef637b374f8 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:09.777205Z

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Observation 338a7bd0-8533-48cc-b072-52b0782fa78e · outbound

This paper cites Cottention: Linear transformers with cosine attention.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Cottention: Linear transformers with cosine attention

Reference 24

Resolution: verified exact; raw_fallback, observed 2026-08-06T13:39:12.674747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17c4307d-a460-412f-bda6-6b8bf80c940e · outbound

This paper cites Rethinking Self-Attention: Towards Interpretability in Neural Parsing.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Rethinking Self-Attention: Towards Interpretability in Neural Parsing

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:39:12.320656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83021d80-2f89-4118-9b5f-0cddb464e601 · outbound

This paper cites Vision transformers are robust learners.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Vision transformers are robust learners

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:10.085428Z

Source-reported events for the cited work

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Observation fb055d8d-21a3-419a-84d6-859cd562eae6 · outbound

This paper cites Improving language understanding by generative pre-training.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Improving language understanding by generative pre-training

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:10.190723Z

Source-reported events for the cited work

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Observation 63e5b590-c494-4205-96df-d5e168bdf421 · outbound

This paper cites Robust speech recognition via large-scale weak supervision.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Robust speech recognition via large-scale weak supervision

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:10.291889Z

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Observation 3b93fb8e-4c74-4dcb-aca6-d41795f98465 · outbound

This paper cites Theory, Analysis, and Best Practices for Sigmoid Self-Attention.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Theory, Analysis, and Best Practices for Sigmoid Self-Attention

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:10.426250Z

Source-reported events for the cited work

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Observation 1c0dedde-95e7-4240-995e-f60e99e48247 · outbound

This paper cites A Generalist Agent.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations A Generalist Agent

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:10.522584Z

Source-reported events for the cited work

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Observation 93f6a0de-7e0c-4a79-815b-c8d64170f2c3 · outbound

This paper cites Sinkformers: Transformers with doubly stochastic attention.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Sinkformers: Transformers with doubly stochastic attention

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:13.796855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a10c65a9-c1e8-49c2-acfb-b45946385079 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.028293Z

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Observation 0775cff6-db90-4bcc-85b5-e8c23c85225d · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1): 1929–1958, 2014.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1): 1929–1958, 2014

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:13.567812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1aae96fa-9a07-4f43-9be8-42ce4733c9e2 · outbound

This paper cites Going deeper with convolutions.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Going deeper with convolutions

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.337832Z

Source-reported events for the cited work

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Observation 5cd4e59c-24fb-4e2c-a0fb-6e0998e46276 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.420842Z

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Observation f8afc304-6734-488c-93a5-9858e8529c69 · outbound

This paper cites Going deeper with image transformers.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Going deeper with image transformers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.508627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c002362e-e902-4d44-a217-e30dc6a985b5 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.592745Z

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Observation 8e0abeff-b491-4245-9253-cf4cb223b71e · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Linformer: Self-Attention with Linear Complexity

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.691599Z

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Observation 531b80a2-f7b0-496f-83b6-a5eaf0c6387e · outbound

This paper cites Xlnet: Generalized autoregressive pretraining for language understanding.Advances in neural information processing systems, 32, 2019.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations Xlnet: Generalized autoregressive pretraining for language understanding.Advances in neural information processing systems, 32, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:13.289835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a2d5546-4a36-4862-90a1-54095ba2982a · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations mixup: Beyond Empirical Risk Minimization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:11.853587Z

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Pith citing papers

Observation 6c55fbb0-586e-4a8d-b505-a02be515a64a · inbound

CLIMP: Contrastive Language-Image Mamba Pretraining cites this paper.

CLIMP: Contrastive Language-Image Mamba Pretraining Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T11:22:14.762729Z

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Observation 55d62c7f-84ec-4740-825a-6bdaab62d682 · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:49.632661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.