{"as_of":"2026-08-09T20:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a25d377863479fc73d36365e5478f34a3c6c1d7261223e3a0779a2dae81b081b","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":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":31,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T17:03:50.087516Z","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:f0a0ac1a61fefde949ee262f2506f9cbc8c13099c8ea2f5aaead2d69b504b0a2","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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:fc3d67a4ebef8abc1eac5de0e048b1e751d710358cd3a98c6d3cceb329d18355","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:1704c6e64b7be57d16067281ff5a1aee94e5be0a7f4d0ac3f1a1d2f5e3ebc286","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:970f3c68fef92da0bf8953b3a5b5d5ee0d205842e2b962fdcf817098c86be9b2","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T17:03:50.087516Z","title":"How to train your vit? data, augmentation, and regularization in vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.06029","last_updated":"2025-06-01T13:34:36Z","snapshot_observed_at":"2026-08-09T04:47:46.789215Z","submitted_at":"2025-02-09T21:05:11Z","title":"DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T17:03:50.087516Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2502.06029"},"observation_digest":"sha256:c136f95cb207fc1231184a606e5287f5c062bc1d1928eafbd1bc9553ddf55042","observation_id":"2ff296f9-cc39-425d-85f6-cb943fced636","resolution":{"observed_at":"2026-08-08T17:03:50.087516Z","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":"2026-08-08T14:37:59.632356Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.06708","last_updated":"2025-02-10T17:37:34Z","snapshot_observed_at":"2026-08-08T14:32:44.705304Z","submitted_at":"2025-02-10T17:37:34Z","title":"TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T14:37:59.632356Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2502.06708"},"observation_digest":"sha256:413d34b2bc6b2569cf12f58f19f4b09543c10e868aeb59cbaa392c1ee27872f8","observation_id":"69e3555e-2820-42fc-8470-3b0b8cea1dab","resolution":{"observed_at":"2026-08-08T14:37:59.632356Z","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":"2026-08-08T12:12:30.787739Z","title":"Steiner, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.07617","last_updated":"2026-05-31T18:45:03Z","snapshot_observed_at":"2026-08-08T12:06:14.483667Z","submitted_at":"2025-02-11T15:05:33Z","title":"Scaling Pre-training to One Hundred Billion Data for Vision Language Models","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-08T12:12:30.787739Z"},"links":{"cited_paper":"/paper/2106.10270","citing_paper":"/paper/2502.07617"},"observation_digest":"sha256:ab2018acf64a5f8989088f3352e204420bae0cbae5d96ca427d74d719ca7c6ff","observation_id":"255794bd-9d13-46bb-a48b-df6496b4b83c","resolution":{"observed_at":"2026-08-08T12:12:30.787739Z","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":"2026-08-07T14:46:53.729509Z","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":"2026-08-08T09:36:17.755126Z","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":"sha256:16a713823ff18ca57fdae33011ff27afacf8ab1cc87ff8131fa1c2eaa230801d","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":"2026-08-09T05:41:52.110094Z","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":"sha256:6e6b3f2e77cf2f6b354e7dd28464c5f8ee9968b809dd5af0c6efad8987e9c531","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":"2026-08-09T17:58:30.461236Z","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":"sha256:2b94889702f0fde3986480fbaec07267839bec4d36cc61aa3032ab6ecb1a5ad4","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":"2026-08-09T03:57:04.486723Z","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:c94ada90c5040c8099f5d5003589f103052ad2fdf174953befb58dd235b76e60","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":"2026-08-09T12:21:21.258157Z","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":"sha256:2395681c5d96b016bdf3a0f085a3295ef1c66af9fedd69f3ba3cd5f5310df795","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":"2026-08-08T06:20:37.657963Z","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":"sha256:495fc1d34434f6a7988ec5bc55dc3ab092ff439a3b5e8febf29e24ede12919f4","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":"2026-08-08T11:29:05.187693Z","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:dc001497209967867a1a8e163ad5420dba3c21cbbfb3f8b4338444919f3740cd","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":"2026-08-09T17:22:10.206554Z","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":"sha256:990bb9f2526d6374f36fc594e12717f5200a67a8a736e7f9ab361a78d96072e6","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":"2026-08-09T09:05:04.155473Z","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":"sha256:70f43eb3dd9b8b59c502af22b9c40ee09626e3120f6fb7e8739e43909d3e80ec","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":"2026-08-07T22:42:44.531320Z","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:e9173bd40329ad102c98d0893d0077e0f0cf79dad61b9c82f0aaed144495b729","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":"sha256:53203cfcaf7e2b0399c5649f72c0e4978e571f7a5dab3551b67f9021f253410b","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:1eaeba22dea5998f2ccd277288ce587aa4e6a4c66264df8249d49a4297da400d","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:9c3b716e71e91213f49faff01a70de8afc5a8349b05b016eff91bc6f8315a504","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:1e60597992cc15823f43c3d2c654369a477a14c102f896a6aea1eb259282845b","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:6730b5ae929cf38eb99cdf3cc6a76476c36aa60b299883bb1046cf5dce4fdcdf","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"sha256:3bb09dca2c8b7a5b879f1c8f242143171dbf37692ed0bbe50467df2176a4d1a9","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T08:26:53.250823Z","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":"sha256:0baa7f57da77e3688fec22c4dda74ab545afba20a7507afa0382260a9be621ef","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":"2026-08-08T11:56:28.562049Z","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":"sha256:4058d8b95c7f0a5b6bf06fed69e40f50bd33813c4af49401f8cb7a5a3452c3d2","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":"2026-08-09T00:57:27.934156Z","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:c25588695ea29f46306cac725d13be6c29afc20eae3d4a71f6e0f3fc87cf0485","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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2106.10270."}