{"as_of":"2026-08-13T05:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e71608b92629ccafb51ddf7a8d97ffeecee7af390d8d3696010d3aecc86e02ac","coverage":[{"denominator":83,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":83,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:50:16.673913Z","state":"measured"},{"denominator":83,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":83,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.03782/citation-record","integrity":"/paper/2501.03782/integrity","json":"/paper/2501.03782/citation-record.json","paper":"/paper/2501.03782"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T02:40:23.887636Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-10T21:50:16.219508Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.219508Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:6c10e582111488607474c596445d6f8ca44c2c4d42f4e24ff09c175c4845e1af","observation_id":"dda057f2-b555-46e6-88a2-0d466bdb7f73","resolution":{"observed_at":"2026-08-10T21:50:16.219508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.227268Z","title":"Maxvit: Multi-axis vision transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.227268Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:bb8d9607564554b3d621c6286cbccc98586a2b52eb17b353a042c5cdb1c41f51","observation_id":"eff23acc-b53b-44ca-aea1-fc3e53ccc33e","resolution":{"observed_at":"2026-08-10T21:50:16.227268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.234330Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.234330Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:2b3d375d9bcc8dd98db74111eb472d4db5d1277cfd3fd124788750abe9bfadf7","observation_id":"e8138482-d2e9-49e9-a492-678f3879cc10","resolution":{"observed_at":"2026-08-10T21:50:16.234330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.240435Z","title":"Exploring plain vision transformer backbones for object detection","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.240435Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:c610df8a1968ad5ddb18b382b0ebaeec0b6d8e85eb6c11c1827c94b8150658bc","observation_id":"4852ba62-e124-44fa-8dfe-9dd4607673c4","resolution":{"observed_at":"2026-08-10T21:50:16.240435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:21.074763Z","title":"Segvit: Semantic segmentation with plain vision transformers.Advances in Neural Information Processing Systems, 35:4971– 4982, 2022","venue":null,"work_id":"e17e9f57-9eea-4122-9875-9e9b213e4001","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.245365Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:6a2cff49a412c14bd8273350ca394f197958fc6bb1a389e6c1ad8c397ee67a2e","observation_id":"e19124bf-dd13-4476-820e-92c494f8be46","resolution":{"observed_at":"2026-08-10T21:50:21.114779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.961493Z","title":"Autoformer: Searching transformers for visual recognition","venue":null,"work_id":"0470535f-9120-4724-ad42-5a67a4694c9f","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.250849Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:844c2ea4734fde5e30df6fcf7aeb3c307eaf9417bf8066cad5b48730f159ca69","observation_id":"3b676842-4448-4531-8737-68fb8b899845","resolution":{"observed_at":"2026-08-10T21:50:20.996917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.841264Z","title":"Prenas: Preferred one-shot learning towards efficient neural architecture search","venue":null,"work_id":"06800155-8e1f-4b1d-bc30-7789e1012ad6","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.255903Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:66d29887fc1822aa1be83fb9675e11082e21e4380d7bbe09891eb203304e95f7","observation_id":"bedc928b-dbe3-4819-8007-b5381849a705","resolution":{"observed_at":"2026-08-10T21:50:20.864880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.741676Z","title":"Vitas: Vision transformer architecture search","venue":null,"work_id":"4d7a463a-14b5-4168-a587-764dbce0504b","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.261218Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:8f1f9a437a42d3e29c073326f45a8b52fd63f89f747f7c6a54033796471bcdb5","observation_id":"a5bc895b-fb10-4921-8040-673d26032db2","resolution":{"observed_at":"2026-08-10T21:50:20.757130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.691203Z","title":"Elasticvit: Conflict-aware supernet training for deploying fast vision transformer on diverse mobile devices","venue":null,"work_id":"96cdbfed-7d12-4b8c-899e-68d126d8e5f6","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.266462Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:670addf62b885c9c5c06678afc13a324d8b7b4d2d3cca85cd1e66db964c59ee6","observation_id":"efebd209-1ab8-46c9-8d1e-23df2c05dd73","resolution":{"observed_at":"2026-08-10T21:50:20.712744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.554760Z","title":"Training-free transformer architecture search","venue":null,"work_id":"e821d008-1b7b-464b-a1bb-ed645cceaf9e","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.274833Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:69fe70a66c7a5dcfe60873c296380041ae65139fede612276f6fdf0da5524991","observation_id":"7cbdcf7b-e1e0-4bb7-8934-6c6d64e7304f","resolution":{"observed_at":"2026-08-10T21:50:20.597320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.464753Z","title":"Training-free transformer architecture search with zero-cost proxy guided evolution","venue":null,"work_id":"ea9fde7a-21bd-49d8-81e5-31ab57447d49","year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.279703Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:76415122ce6031782fd400cf6250b258d658f1e17c843f79691aa864dba49ef6","observation_id":"3e9904df-59e3-48c3-b529-2ceabe4bb7fb","resolution":{"observed_at":"2026-08-10T21:50:20.482671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.401801Z","title":"Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training","venue":null,"work_id":"1924bbac-695e-4dc8-aec4-48d7c4cd6dc9","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.284496Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:d7ea3cd6725a4b194b4729df137985225a5fb4684f51b2fe98d2381f8006db99","observation_id":"c63ce24a-4248-4994-8bb6-fb607c067a25","resolution":{"observed_at":"2026-08-10T21:50:20.407661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.350132Z","title":"Understanding Robustness of Transformers for Image Classification","venue":null,"work_id":"ab08d019-0603-451f-90d4-d8825bd4808b","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.289482Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:4c6713ddc148ed716506c13d48186db15516bdd6e083784ff6f332f3a7361a53","observation_id":"41a81093-0b82-4418-8bf4-9d040d662dd0","resolution":{"observed_at":"2026-08-10T21:50:20.355760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.05211","last_updated":"2022-01-14T03:19:06Z","snapshot_observed_at":"2026-08-11T07:47:58.054053Z","submitted_at":"2021-09-11T08:01:14Z","title":"RobustART: Benchmarking Robustness on Architecture Design and Training Techniques","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.05211","snapshot_observed_at":"2026-08-10T21:50:16.294198Z","title":"Robustart: Benchmarking robustness on architecture design and training techniques","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.294198Z"},"links":{"cited_paper":"/paper/2109.05211","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:13fbfa91937f8d2344c68780331cbb865d7f81a793532e81317428445a6e3e21","observation_id":"e8a91d09-f1c4-4921-a14c-fbfb42a3169b","resolution":{"observed_at":"2026-08-10T21:50:16.294198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10750","last_updated":"2023-10-08T22:01:52Z","snapshot_observed_at":"2026-07-06T14:44:38.168186Z","submitted_at":"2023-01-25T18:14:49Z","title":"Out of Distribution Performance of State of Art Vision Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.10750","snapshot_observed_at":"2026-08-10T21:50:16.299891Z","title":"Out of distribution performance of state of art vision model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.299891Z"},"links":{"cited_paper":"/paper/2301.10750","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:227ab0450498d094c8c0d55d33d66c79db3806489613930d4792689419dd00d0","observation_id":"41630af0-c157-4a13-b4a7-a8ef771b1364","resolution":{"observed_at":"2026-08-10T21:50:16.299891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.333622Z","title":"Searching the search space of vision transformer","venue":null,"work_id":"653013e2-1eda-487d-958a-0814bd94606c","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.305355Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:3ba97e8d8060ba6b0442fc2cbb9493f452153f61437389a78a14d806bf85b48b","observation_id":"813780c7-754b-443a-a286-d43517115a62","resolution":{"observed_at":"2026-08-10T21:50:20.338503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.283788Z","title":"Auto-prox: Training-free vision transformer architecture search via automatic proxy discovery","venue":null,"work_id":"8b0d5df3-aa6b-4214-ad3c-7e4c227a3e52","year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.309950Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:5f7612d344edae0fc61b0c7cbab2e69a516008c81098aa9cbd5b896792e2832e","observation_id":"e4afe3de-2464-4c0e-a803-0621076d6514","resolution":{"observed_at":"2026-08-10T21:50:20.304757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.194832Z","title":"Hr-nas: Searching efficient high-resolution neural architectures with lightweight transformers","venue":null,"work_id":"04e2216a-eb30-4796-9b2b-7519b623d985","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.314748Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:2edbdedbf93aac974690496711aee7246c643c665b531a8dc371093ca7a94036","observation_id":"041367de-f1de-4be5-aed4-c5e4621cde88","resolution":{"observed_at":"2026-08-10T21:50:20.221506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.127013Z","title":"Uninet: Unified architecture search with convolution, transformer, and mlp","venue":null,"work_id":"79b901dc-a585-4630-91d7-f4ea3f117463","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.320177Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:e9eacf0f410ca277dabb6b542dd576577545e4409f8a82c5855e38eb475d717e","observation_id":"d3261a22-aa2b-418e-92a4-348b2588a77a","resolution":{"observed_at":"2026-08-10T21:50:20.132243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:20.042899Z","title":"Bignas: Scaling up neural architecture search with big single-stage models","venue":null,"work_id":"9b5b34d4-de03-4447-9f02-1cb85207bc40","year":2020},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.326488Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:d557338cb1352fb9d81cddae3ece9ff33d49ab41485fff74e12cd8c66b7108ca","observation_id":"f8798990-757e-4e3a-8f79-44dd538f1348","resolution":{"observed_at":"2026-08-10T21:50:20.093471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.944748Z","title":"All tokens matter: Token labeling for training better vision transformers","venue":null,"work_id":"edd1d00b-916a-4324-a7b2-77c3a1ea80c6","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.331505Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:41836ee4458258282fd7ce39e4f01dbbfbb90a1c3020abf9e01dc0514d92d33d","observation_id":"5d3f950c-cb3a-4e15-a827-65c00f19797f","resolution":{"observed_at":"2026-08-10T21:50:19.973926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.794752Z","title":"Crossvit: Cross-attention multi-scale vision transformer for image classification","venue":null,"work_id":"cf93d46f-94a7-4462-ab29-e18472728cb2","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.337975Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:b02e0ef022e69d6fdad01bfb9b963b13ba0848e612eaa313c993e2e2aa6b5562","observation_id":"204eed73-574c-40cc-8006-272749084177","resolution":{"observed_at":"2026-08-10T21:50:19.834755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.616964Z","title":"Pre-trained image processing transformer","venue":null,"work_id":"42097cb2-a84c-4de1-8a98-d8f929e6f3ab","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.346443Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:5389c6ccf03fdcbe84e3822f7e16ca3e3a60ec93b38c2e078412801e85dd6d7a","observation_id":"3c2f6af6-dc51-41bb-bed6-ca063ca99a20","resolution":{"observed_at":"2026-08-10T21:50:19.674755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.12261","last_updated":"2019-03-28T20:56:37Z","snapshot_observed_at":"2026-08-09T13:36:49.416146Z","submitted_at":"2019-03-28T20:56:37Z","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.12261","snapshot_observed_at":"2026-08-10T21:50:16.352204Z","title":"Benchmarking neural network robustness to common corruptions and perturbations","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.352204Z"},"links":{"cited_paper":"/paper/1903.12261","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:af3ca406c3cb28232a851d69dd292b49c0b58182a10978f6c148b20ba5058b68","observation_id":"dc41278a-f203-4abe-9b5e-4ec5c7e68349","resolution":{"observed_at":"2026-08-10T21:50:16.352204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.357201Z","title":"Natural adversarial examples","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.357201Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:9ccd17b7b6ad214b205d7e11b9f49ed3c20dcd942ac24aae6bfab1122b2ba44d","observation_id":"9740dcaf-0109-4894-911c-482a8208822c","resolution":{"observed_at":"2026-08-10T21:50:16.357201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18775","last_updated":"2024-03-27T17:23:39Z","snapshot_observed_at":"2026-08-13T00:43:39.183613Z","submitted_at":"2024-03-27T17:23:39Z","title":"ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18775","snapshot_observed_at":"2026-08-10T21:50:16.362830Z","title":"Imagenet-d: Benchmarking neural network robustness on diffusion synthetic object","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.362830Z"},"links":{"cited_paper":"/paper/2403.18775","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:3dfd2619dd32ce802506e5a5d07e0eff3870efd8e45f7acac8ff38a08f28be4b","observation_id":"3858147f-7060-4892-ac81-d095f7827a5e","resolution":{"observed_at":"2026-08-10T21:50:16.362830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.368894Z","title":"The many faces of robustness: A critical analysis of out-of-distribution generalization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.368894Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:883a3546edced3930163a7aa61f038b4f9b9fa80982fd9d21a307d73c477090b","observation_id":"9149b650-eb33-425c-915c-c54d6e10d5f4","resolution":{"observed_at":"2026-08-10T21:50:16.368894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.374814Z","title":"Learning robust global representations by penalizing local predictive power","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.374814Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:f2b54546ef0c2aa4dc8076c09f02e7d4f54363ef659dd5b12fbd09a8ac028f36","observation_id":"35075d6f-8f8d-4958-a492-fc6ec6ce37b1","resolution":{"observed_at":"2026-08-10T21:50:16.374814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.12231","last_updated":"2022-11-09T23:15:15Z","snapshot_observed_at":"2026-08-02T03:51:09.933624Z","submitted_at":"2018-11-29T15:04:05Z","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.12231","snapshot_observed_at":"2026-08-10T21:50:16.380516Z","title":"Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.380516Z"},"links":{"cited_paper":"/paper/1811.12231","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:d3f1b546e64b1d6fbbbc1e6085422b49a1e3e7f50084c0d983087ec5e3995e89","observation_id":"c1caff34-4ba6-4e99-9835-1a029bc5acba","resolution":{"observed_at":"2026-08-10T21:50:16.380516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.444845Z","title":"HYPO: Hyperspherical out-of-distribution generalization","venue":null,"work_id":"8d4e16e1-1c4c-4bcb-8151-374315b18126","year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.385574Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:993f40d9643f3bf45bc194baada1dcc5f544aaeeeda27f02b5ff4a4994eb4fc8","observation_id":"3923e7c0-c1b0-47b8-94af-045b33d2c0c8","resolution":{"observed_at":"2026-08-10T21:50:19.463977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02893","last_updated":"2020-03-27T19:07:58Z","snapshot_observed_at":"2026-07-06T08:05:24.076802Z","submitted_at":"2019-07-05T15:26:26Z","title":"Invariant Risk Minimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02893","snapshot_observed_at":"2026-08-10T21:50:16.390677Z","title":"Invariant risk minimization","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.390677Z"},"links":{"cited_paper":"/paper/1907.02893","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:10660f6813832b3d2bb324d145a814ec1d6224d1501ded4c0102c091b1403d76","observation_id":"520ea0b8-69b5-4386-93fc-2fdb652dbb8f","resolution":{"observed_at":"2026-08-10T21:50:16.390677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.382618Z","title":"Domain generalization for object recognition with multi-task autoencoders","venue":null,"work_id":"26ff5a29-fd41-46c2-bfdf-8c496c563f93","year":2015},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.395431Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:d559ea79a1d2168c0a7e793748e4216b3fb15096686d4bbcd1a008be3c37d1e1","observation_id":"13cd17ab-06e6-4362-b7d5-30253e478083","resolution":{"observed_at":"2026-08-10T21:50:19.398350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.367058Z","title":"Domain generalization using causal matching","venue":null,"work_id":"ae85c4f3-a266-409a-ae7f-34d5f8a50917","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.400220Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:1e3d9e82c73d3099cd66e8dd4fb11127288813a87d420d06072aa8176f8a50c6","observation_id":"5173e668-3ba9-4e68-950e-83e4051330ba","resolution":{"observed_at":"2026-08-10T21:50:19.371581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.339666Z","title":"Domain generalization via invariant feature representation","venue":null,"work_id":"4e5036b5-2ef9-4c07-afd8-8068a9575d09","year":2013},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.404508Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:801204da9c5a7b84de026a492eb6e138aa652058f2d8193cd22ae7b2d14f05ea","observation_id":"a77b59ee-a555-4f00-87bf-f48701d04dd6","resolution":{"observed_at":"2026-08-10T21:50:19.351314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.314236Z","title":"Fishr: Invariant gradient variances for out-of- distribution generalization","venue":null,"work_id":"c6c97b71-6422-4ff8-8651-b7c29e68bdf7","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.410540Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:4e37c237eb67a35b29fbfcc4a8c3b264f653156ae50c2d79344f177e03419914","observation_id":"ac9c4dd8-750b-4d56-ab68-8cdc7740404a","resolution":{"observed_at":"2026-08-10T21:50:19.326399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.267167Z","title":"Invariant models for causal transfer learning","venue":null,"work_id":"8c6bea6f-7dfe-4fe0-927c-471ac4332983","year":2018},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.415846Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:1c7bc9740881eff17de33360fb61ddd15262b8b7efb398d4e3e486ebeddb2cce","observation_id":"e09058fe-07f4-42f7-ba9f-7f13943ad77c","resolution":{"observed_at":"2026-08-10T21:50:19.281518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.09937","last_updated":"2021-07-14T00:07:51Z","snapshot_observed_at":"2026-08-06T20:03:26.839485Z","submitted_at":"2021-04-20T12:55:37Z","title":"Gradient Matching for Domain Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.09937","snapshot_observed_at":"2026-08-10T21:50:16.421275Z","title":"Gradient matching for domain generalization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.421275Z"},"links":{"cited_paper":"/paper/2104.09937","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:24c59316dbae6988b0e714cc012e6bd1af89a40d639ebb936f2ca59f30b44c5b","observation_id":"4fcb05eb-28ff-432b-8c7c-445dd8ac7b50","resolution":{"observed_at":"2026-08-10T21:50:16.421275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.08731","last_updated":"2020-04-02T05:40:29Z","snapshot_observed_at":"2026-08-02T19:28:48.954133Z","submitted_at":"2019-11-20T06:43:41Z","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.08731","snapshot_observed_at":"2026-08-10T21:50:16.427738Z","title":"Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization.arXiv preprint arXiv:1911.08731, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.427738Z"},"links":{"cited_paper":"/paper/1911.08731","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:72b4b990d44dd88a65e1a8880533614fd2497b014f61ac35508ba0f7866179e2","observation_id":"64a4f131-b804-4314-aed1-105d8e277f20","resolution":{"observed_at":"2026-08-10T21:50:16.427738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.164750Z","title":"Learning to generate novel domains for domain generalization","venue":null,"work_id":"4d4f819e-2c4f-4ba2-9968-d8d4d0ea07f9","year":2020},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.435250Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:7a7254f6606870eda517b7f6f9a9eb2b7f1edd3c2d2ed9af075e674ff46779b7","observation_id":"e59e9aea-f456-4872-ac93-00067df16d30","resolution":{"observed_at":"2026-08-10T21:50:19.191392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.125224Z","title":"Explore and exploit the diverse knowledge in model zoo for domain generalization","venue":null,"work_id":"1889cb8a-47f8-4408-becc-ce81a57a81a7","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.441627Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:a70289d852244a5482bce810911547aa6c891bb93c48f16dc478bef1da38d4eb","observation_id":"d4a237f0-01eb-4a6b-a412-e9ea124338a4","resolution":{"observed_at":"2026-08-10T21:50:19.130570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.102899Z","title":"Model ratatouille: Recycling diverse models for out-of-distribution generalization","venue":null,"work_id":"ba65a5c1-c294-447b-ba8e-bff241bc9ce5","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.446763Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:30f97c4ba2e05760206571a120a76c7361c3ed418e7fce0264e4eb1d7e917d4d","observation_id":"61ccb511-afc3-4d75-bd0e-003d817df51c","resolution":{"observed_at":"2026-08-10T21:50:19.109049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:19.075709Z","title":"Test-time style shifting: Handling arbitrary styles in domain generalization","venue":null,"work_id":"0bd58f96-6ab4-4ed9-ac25-3b3d37c82ee8","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.451893Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:0d7801f64ab35a82bb1e5feafab2be8be1bd522048ecc26b8ab35c8e8f696a87","observation_id":"fe8965fd-5fe5-43ce-8052-bad7dcb3909c","resolution":{"observed_at":"2026-08-10T21:50:19.082348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.458072Z","title":"Improved test-time adaptation for domain generalization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.458072Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:c8e06ae2c07c62c1ebc55e38bb5bccf3cc627ac0d246134c45598a18b454c567","observation_id":"678e0f9b-99ac-4a05-b128-1fa6f2aeabf8","resolution":{"observed_at":"2026-08-10T21:50:16.458072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.462585Z","title":"Reducing domain gap by reducing style bias","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.462585Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:c270bef2abb40281d78cdeca8afecbca7128ee80ce0efa585c02e5d9204c45d6","observation_id":"918955a9-7bc3-4d72-ae9a-465eda3c2921","resolution":{"observed_at":"2026-08-10T21:50:16.462585Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.955110Z","title":"Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection","venue":null,"work_id":"57fafcc9-4410-455f-9dc5-7807e592fb3a","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.467298Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:a065b972d7a6ee86e5cc4a0f43274309eea827222960c5183c025f5ebe02be38","observation_id":"fd018d75-e760-4f75-a481-c9deb0617251","resolution":{"observed_at":"2026-08-10T21:50:18.968303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.860686Z","title":"Selfreg: Self-supervised contrastive regularization for domain generalization","venue":null,"work_id":"0e15c78a-8dbd-4a18-a6c5-5803225f279b","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.472774Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:4f2765a10522302c18ccb49646b95b67517a0c51631193201caec29597a4b0be","observation_id":"fec33ed6-f147-4fbb-b104-3f325ee3e536","resolution":{"observed_at":"2026-08-10T21:50:18.884151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.814781Z","title":"A simple feature augmentation for domain generalization","venue":null,"work_id":"a57e9280-b0a3-4132-85f3-53f98a236793","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.477826Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:e6d2358f481917aae61e9036da6bb17253ad4ca17af78e09de559e00c9d2c4e1","observation_id":"9ba011f3-b161-4663-af04-54818e093903","resolution":{"observed_at":"2026-08-10T21:50:18.824632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02008","last_updated":"2021-04-05T16:58:09Z","snapshot_observed_at":"2026-08-10T09:28:28.538323Z","submitted_at":"2021-04-05T16:58:09Z","title":"Domain Generalization with MixStyle","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.02008","snapshot_observed_at":"2026-08-10T21:50:16.482936Z","title":"Domain generalization with mixstyle","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.482936Z"},"links":{"cited_paper":"/paper/2104.02008","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:6c72a70ee7643a8444a54a292074096d5b8f5cd184ef25d9e1f5454ebf5d664f","observation_id":"f1a36dbb-159a-4adc-a879-54db82f3267e","resolution":{"observed_at":"2026-08-10T21:50:16.482936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.488447Z","title":"A fourier-based framework for domain generalization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.488447Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:1ae33b2cb928120e769b4b4c950241577fb94a3018f2b7b91bea4cbe9813a38f","observation_id":"a43975b7-6cb4-464a-95e3-a94aa14fd60e","resolution":{"observed_at":"2026-08-10T21:50:16.488447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00677","last_updated":"2020-01-03T01:21:27Z","snapshot_observed_at":"2026-08-11T21:33:20.628406Z","submitted_at":"2020-01-03T01:21:27Z","title":"Improve Unsupervised Domain Adaptation with Mixup Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00677","snapshot_observed_at":"2026-08-10T21:50:16.500817Z","title":"Improve unsupervised domain adaptation with mixup training","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.500817Z"},"links":{"cited_paper":"/paper/2001.00677","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:b4c0923b28deeeca6321b5e00f7fe5e62987682414903cc491222584be18a7fe","observation_id":"e0e6b428-4222-47fd-a98f-32dc0ddafc23","resolution":{"observed_at":"2026-08-10T21:50:16.500817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.728681Z","title":"Metanorm: Learning to normalize few-shot batches across domains","venue":null,"work_id":"45cc74ca-b57a-42fe-b325-e308cab5be08","year":2020},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.505632Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:d06c4109784a9d3a7f969d888a15be74e51a4f1c99c8a91210048fbf30611a6d","observation_id":"39dab15f-17ca-4b94-be78-3bc3e98edf68","resolution":{"observed_at":"2026-08-10T21:50:18.739430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.648674Z","title":"Open domain generalization with domain-augmented meta-learning","venue":null,"work_id":"9746bef5-44d6-4cdc-8803-2135d70dbd71","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.510163Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:0e86c12362052bc8b5dfd5dd51d15afac687c98d35e3dcb78a6f07e0a3fc838d","observation_id":"eacbb832-1000-46cc-859e-be0dede24aff","resolution":{"observed_at":"2026-08-10T21:50:18.668328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.537698Z","title":"An investigation of why overparam- eterization exacerbates spurious correlations","venue":null,"work_id":"7e171094-84e1-4463-b7fd-51ed800571b5","year":2020},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.515066Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:65924bd34061bd511a7062f36f5badfa76202009863461ed919399c16b7c7ef4","observation_id":"dc047e47-a193-4468-90c7-8e5470036204","resolution":{"observed_at":"2026-08-10T21:50:18.563945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03635","last_updated":"2019-03-04T15:51:11Z","snapshot_observed_at":"2026-08-05T23:54:27.386622Z","submitted_at":"2018-03-09T18:51:28Z","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03635","snapshot_observed_at":"2026-08-10T21:50:16.519800Z","title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.519800Z"},"links":{"cited_paper":"/paper/1803.03635","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:cc0f12e367e4d88f73c60c50b7c635f120f9a50377086980f54195af2657cd5f","observation_id":"97bd634f-2d01-4648-8b0b-f766d687a267","resolution":{"observed_at":"2026-08-10T21:50:16.519800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.514501Z","title":"Can subnetwork structure be the key to out-of-distribution generalization? In International Conference on Machine Learning, pages 12356–12367","venue":null,"work_id":"05b86482-3eba-47a8-82d7-bccb3ec530a8","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.524602Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:8186e27d3013f972ff8ce4e005550138f18da0d7aaba35983e61034baeccd1ff","observation_id":"46da0219-b39e-44ba-8c7e-6d199e5b9f6f","resolution":{"observed_at":"2026-08-10T21:50:18.527143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.438774Z","title":"Training debiased subnetworks with contrastive weight pruning","venue":null,"work_id":"c74edaef-5366-4ccb-9041-35454d531d5c","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.529140Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:67e12ca2fe3f21a2fde8e08379949762ef6fb40a1d60bc30dfbf1f01bd0abf04","observation_id":"de06ca7d-4348-4337-86a9-d6837dcd110d","resolution":{"observed_at":"2026-08-10T21:50:18.473559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.391734Z","title":"Nas- ood: Neural architecture search for out-of-distribution generalization","venue":null,"work_id":"009b720a-d5b7-4963-b991-6e719be9164b","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.533447Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:86fcde8180fdfcb966778476ae1be8680146562c16c1f79c5c2e66f1f6e44957","observation_id":"41bfd4a9-e13e-42e6-b512-6190e3830d1a","resolution":{"observed_at":"2026-08-10T21:50:18.405620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.373131Z","title":"Alphanet: Improved training of supernets with alpha-divergence","venue":null,"work_id":"914df7d8-452c-4546-9aaa-2a9a6cc23454","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.538645Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:73be74db7790514ae3fbe22eb57c91555e6a721259e4b51e093ff15775efce41","observation_id":"3e8915f9-8bda-4de3-8802-6f3071392a10","resolution":{"observed_at":"2026-08-10T21:50:18.379488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.338155Z","title":"Attentivenas: Improving neural architecture search via attentive sampling","venue":null,"work_id":"35f18628-48a3-4de4-b696-eac3ca985f0b","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.543814Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:91f7571d76bd9893e233056c36b0002e31a49639fe8f8a511d01557b6e11f302","observation_id":"75e33383-0b21-4edf-812a-fac304ef9edd","resolution":{"observed_at":"2026-08-10T21:50:18.344954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.314525Z","title":"Meco: Zero-shot nas with one data and single forward pass via minimum eigenvalue of correlation","venue":null,"work_id":"b874e116-e4df-4275-999b-c6543da3fa18","year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.548816Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:f35349144ab6bbabab6d92bacee79dd5b53e1d4b010fa7878b7f4376fa26ca77","observation_id":"a7cdc349-2461-423d-bcf5-3afd832fce9b","resolution":{"observed_at":"2026-08-10T21:50:18.321183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.06712","last_updated":"2023-06-11T16:02:14Z","snapshot_observed_at":"2026-07-06T15:41:16.963249Z","submitted_at":"2023-06-11T16:02:14Z","title":"Neural Architecture Design and Robustness: A Dataset","version":1},"cited_work":{"arxiv_id":"2306.06712","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.06712","snapshot_observed_at":"2026-08-10T21:50:16.801497Z","title":"Neural Architecture Design and Robustness: A Dataset","venue":"cs.LG","work_id":"b0634496-1c6b-4c76-9b1b-7375b51bb30e","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.553437Z"},"links":{"cited_paper":"/paper/2306.06712","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:ae2ae69d021641e1f872667521c25fa15eb430d1152c220200e49e7121f86c38","observation_id":"dff86e3a-05fc-4141-9e56-a513fd675bee","resolution":{"observed_at":"2026-08-10T21:50:16.808740Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.295897Z","title":"Generalizable lightweight proxy for robust nas against diverse perturbations","venue":null,"work_id":"baf7952f-19b4-43a3-a2ea-a6749d5cef1c","year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.557967Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:faa88b2803b26710383baade144123c8b0f566be73a45ae0e9672ea8f9034635","observation_id":"97e3dd1c-e099-4dad-a74b-1e0a8e04a603","resolution":{"observed_at":"2026-08-10T21:50:18.300945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13134","last_updated":"2024-03-19T20:10:23Z","snapshot_observed_at":"2026-08-13T00:49:59.194319Z","submitted_at":"2024-03-19T20:10:23Z","title":"Robust NAS under adversarial training: benchmark, theory, and beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13134","snapshot_observed_at":"2026-08-10T21:50:16.566292Z","title":"Robust nas under adversarial training: benchmark, theory, and beyond","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.566292Z"},"links":{"cited_paper":"/paper/2403.13134","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:3dba9737731b49053caef5de46c6009a811c811d23b72293de9d0e18a7f2acd6","observation_id":"cea55928-a9d6-4144-9980-b499bc453e98","resolution":{"observed_at":"2026-08-10T21:50:16.566292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.269701Z","title":"Glit: Neural architecture search for global and local image transformer","venue":null,"work_id":"19604055-3021-4844-a8dc-7b6bd106cb2e","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.571526Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:5a9491be37e5ce23b0b61f2595d802036deae292ef6afb62816c40524a68408f","observation_id":"46589c51-c460-4303-9dff-54a269e3cce1","resolution":{"observed_at":"2026-08-10T21:50:18.275690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.221705Z","title":"Shiftnas: Improving one-shot nas via probability shift","venue":null,"work_id":"60e06698-bffa-47eb-bc79-bbbd761b8386","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.576151Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:aa077cef13c76b3f23a3f7d8d434621781930727c63f6f8ddad0452fe97a47d9","observation_id":"0ed26918-2914-4316-bb4c-a21aad362313","resolution":{"observed_at":"2026-08-10T21:50:18.231187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.195568Z","title":"Mdl-nas: A joint multi-domain learning framework for vision transformer","venue":null,"work_id":"e5324294-4c2c-4f9e-8fc1-71eecb27c719","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.580719Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:29e0454bfd63e4db0f3d94fa513b6b49a51468676738a4e3a696dbd2a006bd02","observation_id":"5001420d-40f4-406b-93f8-6916b072ccdf","resolution":{"observed_at":"2026-08-10T21:50:18.204982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.172968Z","title":"Efficient multimodal fusion via interactive prompting","venue":null,"work_id":"7f5bd22c-18b7-4009-b1b5-0b7596553291","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.585560Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:12412b1731fab53939d4de741c7fd5712c4020a136d95fb56702f0dbadc39ad7","observation_id":"032aca59-8501-4b29-a763-be1818d8e16c","resolution":{"observed_at":"2026-08-10T21:50:18.179442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.591737Z","title":"Imagenet: Constructing a large-scale image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.591737Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:97e568bbe880b3ae2401e9b05fe403c6b70840e31947d9b81ffcf1d05a120667","observation_id":"f0ce1b3d-a40d-4687-8e50-33006514b136","resolution":{"observed_at":"2026-08-10T21:50:16.591737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.123114Z","title":"Accuracy on the line: on the strong correlation between out-of- distribution and in-distribution generalization","venue":null,"work_id":"2944e084-dfbf-4158-820f-93fab0329db5","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.596496Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:22ce3c8c58024b51a2f8d95f88a343ccbba8edbeb815dfae71c823336edbb340","observation_id":"46778190-6ce2-4f03-965a-dbbf338a897e","resolution":{"observed_at":"2026-08-10T21:50:18.128319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.106687Z","title":null,"venue":null,"work_id":"fe007c8e-1670-49ad-b6f8-355dc41fffbb","year":2019},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.601475Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:82c460f20973dce9990e2e7ae92d58839c508b28dcd5f3638cd5e852f9587fe1","observation_id":"1ba4234a-2064-48d2-b194-4a6289e17607","resolution":{"observed_at":"2026-08-10T21:50:18.111577Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.080563Z","title":"Id and ood performance are sometimes inversely correlated on real-world datasets","venue":null,"work_id":"34e1849a-1340-4047-8f64-a42088040daa","year":2023},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.605962Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:22a2db360aed4b73b94551b197f1ea99d5a1cfdbff5091cb66f8c91910e23803","observation_id":"5022a401-9088-4ed2-bc96-3caef119be75","resolution":{"observed_at":"2026-08-10T21:50:18.085540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:18.059372Z","title":"Assaying Out-Of-Distribution Generalization in Transfer Learning","venue":null,"work_id":"c9e9ee0e-5a9f-4efc-a7e0-76784cc69133","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.610012Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:9b1f6a81432c24bec714ae9511e3820cb36238a87ca9d6dbab56f1facfbe95d8","observation_id":"3e4ffce9-73b2-47bc-8363-2d7441b82cbf","resolution":{"observed_at":"2026-08-10T21:50:18.068625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.02340","last_updated":"2019-02-23T07:45:29Z","snapshot_observed_at":"2026-07-06T07:06:10.289386Z","submitted_at":"2018-10-04T17:39:58Z","title":"SNIP: Single-shot Network Pruning based on Connection Sensitivity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.02340","snapshot_observed_at":"2026-08-10T21:50:16.614326Z","title":"Snip: Single-shot network pruning based on connection sensitivity","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.614326Z"},"links":{"cited_paper":"/paper/1810.02340","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:34b5844fd280325ebe2083959e5944966be064e28167e5f19a73dd9ae90779f6","observation_id":"95da7d7f-11a7-4077-a25e-de50ba064fab","resolution":{"observed_at":"2026-08-10T21:50:16.614326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.07376","last_updated":"2020-08-07T00:02:33Z","snapshot_observed_at":"2026-08-10T04:53:00.900945Z","submitted_at":"2020-02-18T05:14:47Z","title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.07376","snapshot_observed_at":"2026-08-10T21:50:16.620550Z","title":"Picking winning tickets before training by preserving gradient flow","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.620550Z"},"links":{"cited_paper":"/paper/2002.07376","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:087bb08ec8464ca294ed755bc7984d4c2f2297b9f90ed00cdf8d8566f43b5648","observation_id":"eacf90a0-9af4-4706-a989-d6cc9d384976","resolution":{"observed_at":"2026-08-10T21:50:16.620550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:17.997877Z","title":"Dsrna: Differentiable search of robust neural architectures","venue":null,"work_id":"0a9cae85-4d52-4e94-a1b4-6afdcac27aeb","year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.625483Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:bc4cac606e02ed621003960f4625e081e23f3266073af6ba43c6c45dd1c8237d","observation_id":"5f578d16-e189-412f-ae10-d8d54fa35405","resolution":{"observed_at":"2026-08-10T21:50:18.025700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.629835Z","title":"A new measure of rank correlation","venue":null,"work_id":null,"year":1938},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.629835Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:95bb37e9f527efe6bee9c2f362300ca93e618ea2dab8fbb3d49c34832299476b","observation_id":"8a61f717-92d7-49dc-b0fd-ad2f57bb6adf","resolution":{"observed_at":"2026-08-10T21:50:16.629835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:17.884858Z","title":"Improving vision transform- ers by revisiting high-frequency components","venue":null,"work_id":"8358e236-0ecf-4848-a0a9-65f1dab4bfa2","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.634752Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:f145bd3926a7e2ee214adc27da4be37ba46cb14d027d11bc85ff0244f417a1a9","observation_id":"a9e5173c-a7d4-472f-8d30-b6896a41130c","resolution":{"observed_at":"2026-08-10T21:50:17.924761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.15670","last_updated":"2022-11-02T18:57:19Z","snapshot_observed_at":"2026-08-08T22:57:45.855434Z","submitted_at":"2021-03-29T14:48:24Z","title":"On the Adversarial Robustness of Vision Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.15670","snapshot_observed_at":"2026-08-10T21:50:16.644689Z","title":"On the adversarial robustness of vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.644689Z"},"links":{"cited_paper":"/paper/2103.15670","citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:b152745859d0c601228835d27bc1671ce4ea51262175031943535d21828d95b2","observation_id":"61aebfbf-21b9-405e-8f86-02513cf0fa2a","resolution":{"observed_at":"2026-08-10T21:50:16.644689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:17.811296Z","title":"Can biases in imagenet models explain generalization? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22184–22194, 2024","venue":null,"work_id":"06ed7e1f-9b3d-467f-8b8c-9c8710e29ba1","year":2024},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.649653Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:ff7a2bb8e9d5bf69c261117bb535cd6602c3e66644f4ca7f52c9d6172ec3d016","observation_id":"aa7005ff-c627-459d-ac2c-e9647feeca29","resolution":{"observed_at":"2026-08-10T21:50:17.835816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:17.744751Z","title":"High-frequency component helps explain the generalization of convolutional neural networks","venue":null,"work_id":"7f0ce77d-ec3c-40c0-8e21-58e018d721a4","year":2020},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.655848Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:5ba497e68c8b55fcc534f816d82d84002d45dbab1a7d6f308f4fe23e8007fce7","observation_id":"ac892f40-5522-4188-912b-1a03918237f5","resolution":{"observed_at":"2026-08-10T21:50:17.774859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:16.660640Z","title":"Arbitrary style transfer in real-time with adaptive instance normalization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.660640Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:44422658d9d74c96a88d55cf3925703c8dd83c29a8db6d533ece888d2ffac1ac","observation_id":"8beb5d18-f0f4-4eae-9dd4-3bec116913d4","resolution":{"observed_at":"2026-08-10T21:50:16.660640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:17.554754Z","title":"cat\", \"airplane","venue":null,"work_id":"b7a073bb-f079-4d87-803d-ba28ec883eb4","year":2022},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.667451Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:136a01c1b4dfa26c7f5859bc55e1957cf0e39220ea33ed4cf64eaf36ed53c97d","observation_id":"ca0e1e38-38b0-4173-adb2-666ed1ecda60","resolution":{"observed_at":"2026-08-10T21:50:17.596899Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:50:17.508478Z","title":"These examples could be distorted or unrealistic in object- background placements","venue":null,"work_id":"e3c8c822-2517-4a16-84b0-f7ecf3ce4d80","year":null},"citing_paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T21:50:16.673913Z"},"links":{"citing_paper":"/paper/2501.03782"},"observation_digest":"sha256:17b02b88ecf4a0a749ad125d213c134f718e4e495c6ecfedd16a6f16022a0fb1","observation_id":"17be7581-cca3-47b1-9267-1bcd5d439e43","resolution":{"observed_at":"2026-08-10T21:50:17.515277Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.03782","last_updated":"2025-01-07T13:45:09Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T00:32:56.024830Z","submitted_at":"2025-01-07T13:45:09Z","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights"},"reference_resolution":{"displayed":83,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":1,"verified_fuzzy":51},"total_outbound_references":83},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 0 inbound Pith citation observations for arXiv:2501.03782."}