{"as_of":"2026-08-07T18:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c7e2ca3228294ba28c2d3272cfd45d6cb0c6cf743dce0083910bde04f9b354c3","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":28,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:46:53.729509Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T18:15:59.170440Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2303.15343","last_updated":"2023-09-27T12:05:41Z","snapshot_observed_at":"2026-07-06T15:08:30.190912Z","submitted_at":"2023-03-27T15:53:01Z","title":"Sigmoid Loss for Language Image Pre-Training","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-16T13:05:36.460932Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2303.15343"},"observation_digest":"sha256:568f59f230dde30c38b06a7c58c8fa9936441c0b6c26619159c9726c7d01b533","observation_id":"c20ce74c-047f-410c-ac72-52b7bcc383ed","resolution":{"observed_at":"2026-05-16T13:05:36.491069Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2309.16671","last_updated":"2025-11-23T00:34:43Z","snapshot_observed_at":"2026-08-02T18:24:11.208164Z","submitted_at":"2023-09-28T17:59:56Z","title":"Demystifying CLIP Data","version":6},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-05-16T09:20:20.143143Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2309.16671"},"observation_digest":"sha256:f985f1170ffbbd7eb9f309770a56f7a2f69f8a7f4be75e5690906d7eb52c8821","observation_id":"88d9375c-4005-4576-9c64-077b5c5fa2bc","resolution":{"observed_at":"2026-05-16T09:20:20.323505Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2403.14608","last_updated":"2024-09-16T02:54:50Z","snapshot_observed_at":"2026-08-04T09:07:42.158421Z","submitted_at":"2024-03-21T17:55:50Z","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","version":7},"reference_index":185,"source":"pdf_text","source_observed_at":"2026-05-13T11:32:36.738536Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2403.14608"},"observation_digest":"sha256:ce8ab78de3a8e8a2d1c491941e1449a97ebbf56db00f98fd62f857a465393f24","observation_id":"1a7378d4-cace-4508-b514-cf3741ec5691","resolution":{"observed_at":"2026-05-13T11:32:36.926712Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T12:38:24.425784Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2410.24164"},"observation_digest":"sha256:ca9a93e6e30ccc1daeab72927617f76f895f31645dd1377d5b63ee3161a70e3b","observation_id":"0a682dcd-0704-4295-8db7-3ac96b14c373","resolution":{"observed_at":"2026-05-10T12:38:24.532658Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T14:46:53.729509Z","title":"How to train your VIT? Data, augmentation, and regularization in vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.17665","last_updated":"2025-05-23T09:30:45Z","snapshot_observed_at":"2026-08-07T14:40:38.832822Z","submitted_at":"2025-05-23T09:30:45Z","title":"EMRA-proxy: Enhancing Multi-Class Region Semantic Segmentation in Remote Sensing Images with Attention Proxy","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:46:53.729509Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2505.17665"},"observation_digest":"sha256:71feb70aa506d6e958b65e7c7d49e8a646b31d39323a0fc87bd1e4e742b0fa82","observation_id":"9bc80515-111b-4fcf-8c17-1f8e21331cc1","resolution":{"observed_at":"2026-08-07T14:46:53.729509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T14:15:47.047930Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.19535","last_updated":"2025-05-26T05:47:09Z","snapshot_observed_at":"2026-08-07T14:09:36.879567Z","submitted_at":"2025-05-26T05:47:09Z","title":"TDVE-Assessor: Benchmarking and Evaluating the Quality of Text-Driven Video Editing with LMMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:47.047930Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2505.19535"},"observation_digest":"sha256:deedbc40575daa03053d54a47623936a1995054280575a24a3f5f4c7c3c8d13c","observation_id":"9fc44e78-5762-44f8-b03f-022e60c60f95","resolution":{"observed_at":"2026-08-07T14:15:47.047930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T13:53:12.281412Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20802","last_updated":"2025-05-27T07:06:54Z","snapshot_observed_at":"2026-08-07T13:44:37.360144Z","submitted_at":"2025-05-27T07:06:54Z","title":"Leaner Transformers: More Heads, Less Depth","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:53:12.281412Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2505.20802"},"observation_digest":"sha256:36e4f02c0b80d569534e20ed4f673d439be9f824d2c1a0e84523eee32f932555","observation_id":"764911ee-d831-4bb5-84c0-49aa58cfe182","resolution":{"observed_at":"2026-08-07T13:53:12.281412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T05:48:35.044872Z","title":"How to train your ViT ? Data , Augmentation , and Regularization in Vision Transformers , June 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07378","last_updated":"2025-06-09T02:51:36Z","snapshot_observed_at":"2026-08-07T15:36:04.564218Z","submitted_at":"2025-06-09T02:51:36Z","title":"Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalization","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T05:48:35.044872Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2506.07378"},"observation_digest":"sha256:e80c0cce62997b19d5e17e2cb154e3bde56284f310bb49304f612b40abfceb9a","observation_id":"42585f21-2d84-489f-8b2a-e98b3c9bf0e0","resolution":{"observed_at":"2026-08-07T05:48:35.044872Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T05:25:31.940685Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08008","last_updated":"2025-06-09T17:59:54Z","snapshot_observed_at":"2026-08-07T05:17:55.846802Z","submitted_at":"2025-06-09T17:59:54Z","title":"Hidden in plain sight: VLMs overlook their visual representations","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T05:25:31.940685Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2506.08008"},"observation_digest":"sha256:26c4240078fb3bf8ff6d17e96d29e22391f53362a987d5a5c9a0c47d1d7a527b","observation_id":"96bd96aa-b993-4134-9747-392bb262feb0","resolution":{"observed_at":"2026-08-07T05:25:31.940685Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T05:07:14.444800Z","title":"Steiner, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08735","last_updated":"2025-07-23T10:31:35Z","snapshot_observed_at":"2026-08-07T05:01:02.501134Z","submitted_at":"2025-06-10T12:31:05Z","title":"InceptionMamba: An Efficient Hybrid Network with Large Band Convolution and Bottleneck Mamba","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:07:14.444800Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2506.08735"},"observation_digest":"sha256:fa2dddee9917a3ad53da4940b093745c329896d4bce9011e802513bde602d867","observation_id":"a7c6cb3a-4163-4dd1-afa1-20b9344c294f","resolution":{"observed_at":"2026-08-07T05:07:14.444800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T05:44:09.801949Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.09066","last_updated":"2025-06-08T16:14:37Z","snapshot_observed_at":"2026-08-07T05:37:04.064126Z","submitted_at":"2025-06-08T16:14:37Z","title":"ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:44:09.801949Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2506.09066"},"observation_digest":"sha256:f12ccb740eeea756b833f2a68abe09a89bd104c3a36ffa489d57eabbd61b57ab","observation_id":"3534d411-7f0c-4e82-8175-a40d15f136fb","resolution":{"observed_at":"2026-08-07T05:44:09.801949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T04:39:06.578252Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10084","last_updated":"2025-06-11T18:04:45Z","snapshot_observed_at":"2026-08-07T09:47:08.612752Z","submitted_at":"2025-06-11T18:04:45Z","title":"DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:39:06.578252Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2506.10084"},"observation_digest":"sha256:9202f283d006a1a22dd217547667170c974b457000b6fba51c56f4a234eed25e","observation_id":"f3e75e37-b0af-4a03-8caf-d19a2825939c","resolution":{"observed_at":"2026-08-07T04:39:06.578252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-07T04:08:07.020403Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.11678","last_updated":"2025-06-13T11:16:50Z","snapshot_observed_at":"2026-08-07T04:01:54.710980Z","submitted_at":"2025-06-13T11:16:50Z","title":"Pose Matters: Evaluating Vision Transformers and CNNs for Human Action Recognition on Small COCO Subsets","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T04:08:07.020403Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2506.11678"},"observation_digest":"sha256:59cd5549bb2325bec206bfd6d5dfbc0db3654a1d4f1c6b1d415fc03e659609ed","observation_id":"7c5922de-eae6-47b2-9b43-a83d6eb5ce41","resolution":{"observed_at":"2026-08-07T04:08:07.020403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-06T20:40:38.631360Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers.arXiv preprint arXiv:2106.10270, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02148","last_updated":"2025-07-10T14:55:57Z","snapshot_observed_at":"2026-08-06T20:34:14.751824Z","submitted_at":"2025-07-02T21:06:39Z","title":"Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T20:40:38.631360Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2507.02148"},"observation_digest":"sha256:aed3b481ecdf185809e319c58a8068c54207da57e694a89f6478bb77041e01d2","observation_id":"6b183a73-cc2c-4838-b1c5-c9976c695e2e","resolution":{"observed_at":"2026-08-06T20:40:38.631360Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-04T09:03:09.342772Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers.arXiv preprint arXiv:2106.10270, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.17700","last_updated":"2026-05-29T13:56:08Z","snapshot_observed_at":"2026-08-07T09:18:54.147310Z","submitted_at":"2025-10-20T16:15:03Z","title":"Elastic ViTs from Pretrained Models without Retraining","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T09:03:09.342772Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2510.17700"},"observation_digest":"sha256:9b4f41256a54c55079cb4d8fc110d683b45d61621e75a42cd6f2c7d759a407a6","observation_id":"a2560c90-33d3-4495-af18-358e1fb372a8","resolution":{"observed_at":"2026-08-04T09:03:09.342772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-04T07:01:12.434996Z","title":"https://doi.org/10.48550/arXiv.2106.10270,http://arxiv.org/abs/ 2106.10270, arXiv:2106.10270 [cs]","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.27442","last_updated":"2025-10-31T12:49:13Z","snapshot_observed_at":"2026-08-07T12:01:01.555908Z","submitted_at":"2025-10-31T12:49:13Z","title":"CoMViT: An Efficient Vision Backbone for Supervised Classification in Medical Imaging","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T07:01:12.434996Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2510.27442"},"observation_digest":"sha256:8f7707d90eb57ec16b44ce1e96bf6f2881c425f6fa026d5c7ec6270fef5c7654","observation_id":"272e2d83-974f-48ea-9120-5bf5c4981d82","resolution":{"observed_at":"2026-08-04T07:01:12.434996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-03T23:34:16.095023Z","title":"arXiv preprint arXiv:2106.10270 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.05150","last_updated":"2026-07-02T15:10:58Z","snapshot_observed_at":"2026-08-03T23:34:12.277786Z","submitted_at":"2025-11-07T11:05:36Z","title":"Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T23:34:16.095023Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2511.05150"},"observation_digest":"sha256:a912714c5e83e02822a2b3e15607c8cddc08ff4390cd1c7a98638b5871dcc2b0","observation_id":"f6043aef-f7c3-442b-9bf2-838d20253c18","resolution":{"observed_at":"2026-08-03T23:34:16.095023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2603.13652","last_updated":"2026-05-17T17:44:53Z","snapshot_observed_at":"2026-07-06T22:49:01.769027Z","submitted_at":"2026-03-13T23:25:49Z","title":"Causal Attribution via Activation Patching","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-21T11:09:50.326389Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2603.13652"},"observation_digest":"sha256:77696bc0d94fab098d2e3d84d7ae9bcf5208c6c2cbc2a4f0fdfe2ea1ca2f1454","observation_id":"f42dac14-2c5d-4b95-924e-1dc28f5ab6fc","resolution":{"observed_at":"2026-05-21T11:10:02.082743Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-14T21:34:48.709465Z","title":"arXiv preprint arXiv:2106.10270 (2021) 6","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.13994","last_updated":"2026-07-09T15:26:35Z","snapshot_observed_at":"2026-08-06T21:42:03.113909Z","submitted_at":"2026-03-14T15:43:10Z","title":"Human-like Object Grouping in Self-supervised Vision Transformers","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T21:34:48.709465Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2603.13994"},"observation_digest":"sha256:a3deb89f1fc3e5eb7b75aef70c07c8d76ff6ee7555be561c1ff126afb23f0f5b","observation_id":"376ee791-ad4f-4850-a5d1-1d93161eae35","resolution":{"observed_at":"2026-07-14T21:34:48.709465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2604.18094","last_updated":"2026-04-20T11:10:18Z","snapshot_observed_at":"2026-07-06T23:05:04.279268Z","submitted_at":"2026-04-20T11:10:18Z","title":"Decision-Aware Attention Propagation for Vision Transformer Explainability","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T04:23:04.096579Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2604.18094"},"observation_digest":"sha256:724208065ab3c0c6e2d59f0a2e57a6364b73116d7b4c7740b8926c09e1e1a270","observation_id":"a1b08e9a-7280-4fcb-9bf2-3e635174cd1d","resolution":{"observed_at":"2026-05-11T12:01:01.755658Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2605.14521","last_updated":"2026-05-14T08:05:39Z","snapshot_observed_at":"2026-07-06T23:25:54.070869Z","submitted_at":"2026-05-14T08:05:39Z","title":"Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-15T02:29:49.803834Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2605.14521"},"observation_digest":"sha256:c891273eae7cb373137e78ae67217215dd86b51e597c5a1e9e7683d71cd102a9","observation_id":"b3c06afe-72a1-4860-af0a-567a789be8ec","resolution":{"observed_at":"2026-05-15T02:33:32.625105Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2605.22372","last_updated":"2026-05-21T12:04:49Z","snapshot_observed_at":"2026-08-02T09:32:53.002256Z","submitted_at":"2026-05-21T12:04:49Z","title":"ASAP: Attention Sink Anchored Pruning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-22T08:12:13.451406Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2605.22372"},"observation_digest":"sha256:dc0a37ee155bf2360cf0a30cfe7dabf59fd0519712be78d8022dd982f553d9f8","observation_id":"7494ba04-06cb-448f-aa2a-656cd19c5252","resolution":{"observed_at":"2026-05-22T08:14:45.564776Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2605.23719","last_updated":"2026-05-20T13:35:17Z","snapshot_observed_at":"2026-08-02T10:22:47.382889Z","submitted_at":"2026-05-20T13:35:17Z","title":"Weierstrass Positional Encoding for Vision Transformers","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T05:48:36.533090Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2605.23719"},"observation_digest":"sha256:f44843cfce889802c37070a3148c10f3f09a47d752f40826168b9487a0835046","observation_id":"ae73bce2-3402-4ca3-b4fe-61ef2d1c7484","resolution":{"observed_at":"2026-05-25T05:50:23.743705Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":"2106.10270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-01T18:15:59.170440Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":"b4cdfa0e-9daa-4ba1-9969-c9be2d1caaed","year":2021},"citing_paper":{"arxiv_id":"2606.27527","last_updated":"2026-06-25T20:19:50Z","snapshot_observed_at":"2026-07-07T00:01:44.678954Z","submitted_at":"2026-06-25T20:19:50Z","title":"Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-06-29T02:07:27.600563Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2606.27527"},"observation_digest":"sha256:f843209a025601d136d68774f7a809304b5594bbba5ea3af85370378288dfeb1","observation_id":"20056288-d1b1-4b65-933b-1b176bd3358e","resolution":{"observed_at":"2026-07-01T18:15:59.172041Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-02T03:08:45.376016Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers.arXiv preprint arXiv:2106.10270, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.13983","last_updated":"2026-07-15T16:12:48Z","snapshot_observed_at":"2026-08-05T22:10:02.374626Z","submitted_at":"2026-07-15T16:12:48Z","title":"Screening Is Effective for Visual Recognition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T03:08:45.376016Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2607.13983"},"observation_digest":"sha256:d1edc62f0a00279c3b4cb358f6a2e155aac5f58076f10927036f0d5683caa0e7","observation_id":"ebba460c-3cad-45ec-bfdd-c603b8daea9e","resolution":{"observed_at":"2026-08-02T03:08:45.376016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-01T13:33:15.789562Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.19086","last_updated":"2026-07-21T13:23:42Z","snapshot_observed_at":"2026-08-06T04:12:49.283946Z","submitted_at":"2026-07-21T13:23:42Z","title":"Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-01T13:33:15.789562Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2607.19086"},"observation_digest":"sha256:964764d058b899feddda8b73a7b7dec0a0e7a22e129fc1edf703f2bf26bb2ff8","observation_id":"cbcb636e-fb69-4db7-89e7-e96e75eb7dac","resolution":{"observed_at":"2026-08-01T13:33:15.789562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-07-31T23:27:36.860612Z","title":"Steiner, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.23972","last_updated":"2026-07-27T03:46:11Z","snapshot_observed_at":"2026-08-06T20:48:01.300569Z","submitted_at":"2026-07-27T03:46:11Z","title":"Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI","version":1},"reference_index":186,"source":"pdf_text","source_observed_at":"2026-07-31T23:27:36.860612Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2607.23972"},"observation_digest":"sha256:7f31cc43c47feb263ac10490690faa1ff05093a3dc0ee39db8db257915f1bc50","observation_id":"f1edc681-1762-4852-a8af-99802565b854","resolution":{"observed_at":"2026-07-31T23:27:36.860612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.10270","snapshot_observed_at":"2026-08-01T13:20:38.168263Z","title":"Transactions on Machine Learning Research (TMLR) , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26565","last_updated":"2026-07-30T02:21:33Z","snapshot_observed_at":"2026-08-03T14:53:05.984539Z","submitted_at":"2026-07-29T07:35:59Z","title":"Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification","version":2},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-01T13:20:38.168263Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2607.26565"},"observation_digest":"sha256:560621d3038dd64ba8b0738bc03c12d68a0812116457a365ea87e6a7b1127c1a","observation_id":"8a185ae9-54d4-4b74-bfab-00f1a50c0bd0","resolution":{"observed_at":"2026-08-01T13:20:38.168263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.10270/citation-record","integrity":"/paper/2106.10270/integrity","json":"/paper/2106.10270/citation-record.json","paper":"/paper/2106.10270"},"outbound":[],"paper":{"arxiv_id":"2106.10270","last_updated":"2022-06-23T10:51:04Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T08:18:31.112514Z","submitted_at":"2021-06-18T17:58:20Z","title":"How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2106.10270."}