{"as_of":"2026-08-07T23:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:00fe5e366503aac4f6d73ec363f1276f7ed683cad394f7a53f5dcca67b7d5bc4","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:14:33.918662Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T19:02:26.047252Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-09T06:05:34.771915Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"cited_work":{"arxiv_id":"2506.08518","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08518","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fedtail: Federated long-tailed domain generalization with sharpness-guided gradient matching.arXiv preprint arXiv:2506.08518","venue":null,"work_id":"96267718-ef4b-46c6-ab9a-1f119783a66d","year":2025},"citing_paper":{"arxiv_id":"2605.02183","last_updated":"2026-05-04T03:25:53Z","snapshot_observed_at":"2026-07-06T23:15:18.048504Z","submitted_at":"2026-05-04T03:25:53Z","title":"Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-08T19:02:26.047252Z"},"links":{"cited_paper":"/paper/2506.08518","citing_paper":"/paper/2605.02183"},"observation_digest":"sha256:b8ecba081b3f056c633987cb865d54e78f4328b2312ab40627e117ec693d47fd","observation_id":"b082e14c-4dd9-4a28-8cb7-09cbe3f55694","resolution":{"observed_at":"2026-05-09T06:05:34.773826Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.08518/citation-record","integrity":"/paper/2506.08518/integrity","json":"/paper/2506.08518/citation-record.json","paper":"/paper/2506.08518"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.873082Z","title":"Towards principled disentanglement for domain generalization","venue":null,"work_id":"baa16f09-e199-4e09-9c5f-7acfe0d88462","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.679933Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:0bdc53bbaa5aa41e15c258e2cabc4a4cc4e14ff1f7fd5a6bd1ab37d55082a001","observation_id":"049ab2c1-7057-4d49-a899-5ddf2fee8bab","resolution":{"observed_at":"2026-08-07T05:14:34.877897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.857450Z","title":"Learning to learn single domain generalization","venue":null,"work_id":"6e2b4bde-47ec-422a-8d95-8dbda69cb03d","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.684823Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:1d92c52072a9490b3168fff6f0d182e012eada486340780f394b48c063e8434e","observation_id":"e4948baf-b37a-4262-87c0-d2c844b01c7c","resolution":{"observed_at":"2026-08-07T05:14:34.862985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.842189Z","title":"Balaji, S","venue":null,"work_id":"b60c28f6-ccff-4df5-a007-bcc701dbf6bb","year":2018},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.688785Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:7960a23c9be67d3f8d8cf6f36f4f24094352f6e667e4ec29eb3447c99daae723","observation_id":"b960ed0f-a46b-4410-b682-75606604d479","resolution":{"observed_at":"2026-08-07T05:14:34.846593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.825577Z","title":"Domain generalization via invariant feature representation","venue":null,"work_id":"661df48e-87a7-43ab-b490-d035c1c34155","year":2013},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.693456Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:98ccb636633f6b8c3c30de37a15db8054f8905520868d7fb148f847bb46b17d9","observation_id":"45f1851f-bc09-4ca4-b1bf-034e67bd1c57","resolution":{"observed_at":"2026-08-07T05:14:34.831448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.808497Z","title":"Learning to generalize: Meta-learning for domain generalization","venue":null,"work_id":"f509e97e-ccc3-4c91-9aed-211546f25eb9","year":2018},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.697482Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:ba736ced1d7415c394c7a92a4246b7949237e19909aa59d78d8cae2d4e871073","observation_id":"98558007-f53a-4082-b05f-95da822cff35","resolution":{"observed_at":"2026-08-07T05:14:34.814082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.793532Z","title":"Semantic data augmentation based distance metric learning for domain generalization","venue":null,"work_id":"9af446d8-124d-4e72-9e34-72f6018fb5cc","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.701598Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:92ba4b8ce99f79ff41fa1c42ff819cf12313e8c35725e40a26e8ef61e547f114","observation_id":"55b055e9-3dda-4323-8985-4fdf34e410cc","resolution":{"observed_at":"2026-08-07T05:14:34.798446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.777126Z","title":"Domain generalization via entropy regularization","venue":null,"work_id":"fa2b3ce6-6d1f-4bc0-a6aa-694783f43a32","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.706134Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:cd82e4dc04ea69688ece3079acee684f542769bef579b18e083115e64ee4ee28","observation_id":"71d5530c-e960-47f2-9957-4503cf5b4d07","resolution":{"observed_at":"2026-08-07T05:14:34.782628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.01412","last_updated":"2021-04-29T16:44:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-03T19:02:10Z","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.01412","snapshot_observed_at":"2026-08-07T05:14:33.709857Z","title":"Sharpness-aware minimization for efficiently improving generalization","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.709857Z"},"links":{"cited_paper":"/paper/2010.01412","citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:f20aab193527c7ae3c9d43facdce488d6fdcc541726ded36977685a12ac13234","observation_id":"dff43189-9484-4ab7-9207-28aa9504987a","resolution":{"observed_at":"2026-08-07T05:14:33.709857Z","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":"2026-08-07T05:14:34.761619Z","title":"Escaping saddle points for effective generalization on class-imbalanced data","venue":null,"work_id":"b815d4cc-afeb-49fe-b578-f175a17872ab","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.713906Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:3432ad98ac4099fa54eac8ac0f128bd3806af636f5dc162af3970b53fa1a2267","observation_id":"b6219562-3f00-4476-a594-64a5450711c9","resolution":{"observed_at":"2026-08-07T05:14:34.766705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.745976Z","title":"Domain adaptation for semantic segmentation with maximum squares loss","venue":null,"work_id":"b1584913-39ab-4540-8cde-987c7f817748","year":2019},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.718095Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:b42ef2f48943b3b10319f5bb4ed8add6045d959af49f6fcdb79c08d163fd0c46","observation_id":"c88b8ab5-5d33-4087-97eb-e693c751260f","resolution":{"observed_at":"2026-08-07T05:14:34.750891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.722042Z","title":"Semi-supervised learning by entropy minimization","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.722042Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:02a4d8c470272f8cdd6c5b78de63a40e5d9850628b52f06769702a163e5ca465","observation_id":"ce2f4981-86a5-4c26-ae65-e67196f5324c","resolution":{"observed_at":"2026-08-07T05:14:33.722042Z","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":"2026-08-07T05:14:33.725992Z","title":"Domain-adversarial training of neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.725992Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:e5fb2e51fc82d87a3dffa6af78c6bd007b19ab73b4e5daa95b01af1df2c66776","observation_id":"eb2cfc73-0c12-40ec-9167-7ced98b2dea5","resolution":{"observed_at":"2026-08-07T05:14:33.725992Z","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":"2026-08-07T05:14:34.712059Z","title":"Domain generalization with adversarial feature learning","venue":null,"work_id":"d2fa8b17-8738-4348-9f4b-10c1d0ec9360","year":2018},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.729866Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:f2516bd5c48b21360ec96147d848f59ed7529088dc48681411cc50c9fcae0963","observation_id":"1e695509-688e-45c1-a396-36a594e51f9e","resolution":{"observed_at":"2026-08-07T05:14:34.716628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.695386Z","title":"Learning de-biased represen- tations with biased representations","venue":null,"work_id":"11d1ed29-4de3-42ba-a9b7-a7564608a8d7","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.733430Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:a46b5a46cd0c1d16894a1e2d96b393a0ac112764fae5071a8701feac0e4e21e5","observation_id":"20f3af28-9173-4d47-bb5c-6fab277f8a93","resolution":{"observed_at":"2026-08-07T05:14:34.700842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.679208Z","title":"Adaptive risk minimization: Learning to adapt to domain shift","venue":null,"work_id":"e0fae076-a658-4b36-85d5-301f8af585a2","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.737711Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:4677b1afb896d64c87e86d7026bf05c9615c8ec5603841829a9d4363ae30b3ce","observation_id":"c2b004dc-70bf-42a9-8063-29b57f2d02b8","resolution":{"observed_at":"2026-08-07T05:14:34.684597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.662421Z","title":"Domain generalization via model-agnostic learning of semantic features","venue":null,"work_id":"d38c9a00-8c55-45a0-82c0-61e58af38733","year":2019},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.741646Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:308da82b6e0de9d8224c5869570e70bb17399bb9f79d07df2be7c6afcfa8f54e","observation_id":"d3f68e51-e5b6-456e-8603-00c3ebed5efe","resolution":{"observed_at":"2026-08-07T05:14:34.668529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.645100Z","title":"Domain adaptive ensemble learning.IEEE Transactions on Image Processing, 30:8008–8018, 2021","venue":null,"work_id":"b176b412-9ebf-46f6-90ba-92b1df4156f7","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.745787Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:8ba8174916705dfb14ae2acc8bdeb6532b843492f25b0d35915fa3c1d64aa487","observation_id":"d8ebe0ad-83fe-41ff-bba6-cc6e684fac6b","resolution":{"observed_at":"2026-08-07T05:14:34.651257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.627155Z","title":"Generalizing across domains via cross-gradient training","venue":null,"work_id":"89cdbb05-3160-4496-be11-758563b256f1","year":2018},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.750090Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:2144f3a3b40bbf50b0bae86413138690c377ec044f0e13e67915cde2b32e9848","observation_id":"31c0ca77-c1f5-4774-81e2-55455ad58872","resolution":{"observed_at":"2026-08-07T05:14:34.632946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.611082Z","title":"Domain generaliza- tion by solving jigsaw puzzles","venue":null,"work_id":"3281d6a2-5c48-4ed5-8b76-594d732d2691","year":2019},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.753760Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:e8bd7d0b70540d4c41db3d8d4a183de4e0f7a890d32bb1e578d1df15709b8d72","observation_id":"f63bc897-6f9f-407b-914b-8264a665dbd9","resolution":{"observed_at":"2026-08-07T05:14:34.616066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.594748Z","title":"Domain agnostic learning with disentangled representations","venue":null,"work_id":"45872ba5-2169-4a36-a0c3-3b620c4ae1b6","year":2019},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.757653Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:aa426fdd78846a01d704fa066d12fc3068b939311b01cf86efde230f5aff3bd9","observation_id":"f6f2ef75-3df7-48de-a5f8-2f6d409da64c","resolution":{"observed_at":"2026-08-07T05:14:34.599911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.579954Z","title":"Undoing the damage of dataset bias","venue":null,"work_id":"f5751fd2-c765-41d6-865e-4a5c07b32c04","year":2012},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.761422Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:89d9f69cb28cf5b1563264bf841f744855a36bb3b34a2270b8fecdbc1986cbc6","observation_id":"b49badf1-1bcf-4da7-8a7a-72acef56ed3e","resolution":{"observed_at":"2026-08-07T05:14:34.584648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.564338Z","title":"Cross-domain face presentation attack detection via multi-domain disentangled representation learning","venue":null,"work_id":"69485395-1c0c-4234-81d6-5d362510dafc","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.765080Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:d123fb23586af69a11a39215376c708b164957f3c409cf029b94439da555be01","observation_id":"7610cc33-24a6-4f98-82e4-498431857262","resolution":{"observed_at":"2026-08-07T05:14:34.569123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.769024Z","title":"Out-of-distribution generalization via risk extrapolation (rex)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.769024Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:bac72581d442077e3babed7d03f05ef8ae4ddde0209ddd9396d4ed391ba567fb","observation_id":"0613829f-86d2-4ff9-bd99-934b15c2cba4","resolution":{"observed_at":"2026-08-07T05:14:33.769024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-07T05:14:33.772882Z","title":"Invariant risk minimization","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.772882Z"},"links":{"cited_paper":"/paper/1907.02893","citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:df1886cb315dbeaeb0e5c5a4a201f47187a6212250358035c808368ce1fb5ea7","observation_id":"40ace62e-f138-495c-b298-1dbfc7ba01e9","resolution":{"observed_at":"2026-08-07T05:14:33.772882Z","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":"2026-08-07T05:14:34.537648Z","title":"Domain generalization via gradient surgery","venue":null,"work_id":"989bea0b-5ce4-4427-9f20-d5002ccfbadd","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.777153Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:81f233893230c729cc539af34fa97ce0ad1e0c6f27132369c87e0df1dad06a70","observation_id":"474a707e-2d33-4ae3-8b64-b6ce258d0474","resolution":{"observed_at":"2026-08-07T05:14:34.543438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.520456Z","title":"Towards efficient and scalable sharpness- aware minimization","venue":null,"work_id":"d3f66592-5d30-4a7f-afc0-a8e9a0a56b4e","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.780920Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:4963ca05311099489c1f8f53d9d0a3b1adba49c38a7d80e7369ada4a05ea2ea3","observation_id":"b05dead3-8db3-45a5-a970-eb8d3bfe2316","resolution":{"observed_at":"2026-08-07T05:14:34.526232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.03141","last_updated":"2022-05-28T15:35:32Z","snapshot_observed_at":"2026-08-07T22:49:31.348973Z","submitted_at":"2021-10-07T02:20:37Z","title":"Efficient Sharpness-aware Minimization for Improved Training of Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.03141","snapshot_observed_at":"2026-08-07T05:14:33.784442Z","title":"Efficient sharpness-aware minimization for improved training of neural networks.arXiv preprint arXiv:2110.03141, 2021","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.784442Z"},"links":{"cited_paper":"/paper/2110.03141","citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:883af635401704dc22cf4ef8b02b8be5dc60f81ecf598bf49ebbc7cc83350901","observation_id":"fea65bef-241c-4e69-9eac-953206a2b205","resolution":{"observed_at":"2026-08-07T05:14:33.784442Z","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":"2026-08-07T05:14:33.789010Z","title":"Simplifying neural nets by discovering flat minima","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.789010Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:f44b31ba849e9e2ffaaacbda90e96700691ac07f9d9f1a3862d8a63cf98fd720","observation_id":"624d08ff-0bab-431f-b254-902ccb16c0d0","resolution":{"observed_at":"2026-08-07T05:14:33.789010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.04836","last_updated":"2017-02-09T20:38:16Z","snapshot_observed_at":"2026-07-06T05:10:58.923264Z","submitted_at":"2016-09-15T20:03:06Z","title":"On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.04836","snapshot_observed_at":"2026-08-07T05:14:33.792965Z","title":"On large-batch training for deep learning: Generalization gap and sharp minima","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.792965Z"},"links":{"cited_paper":"/paper/1609.04836","citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:6e40ff2f616ae4a30482f958cf69cccf86245bf7b251e545657648e191651805","observation_id":"9bc757ae-6693-4fa7-8a77-e6b961accbbe","resolution":{"observed_at":"2026-08-07T05:14:33.792965Z","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":"2026-08-07T05:14:34.495560Z","title":"Sharp minima can generalize for deep nets","venue":null,"work_id":"2d467254-039a-4c5a-8de5-3cf4de9a321e","year":2017},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.796853Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:d2b9b126c8e8ed91eb803ff788b6ab098a09b6efb6978d9aab1eaf1393eb5fd2","observation_id":"ee9efb04-0799-4f6a-a7a7-273c8880a280","resolution":{"observed_at":"2026-08-07T05:14:34.500494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.479208Z","title":"Swad: Domain generalization by seeking flat minima","venue":null,"work_id":"0fb02e52-59c7-445a-a54c-507f635e0f1d","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.800598Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:d856c2bf9c6637b85cb94733f858a6e5f6470ab3c750c028098419667bde604d","observation_id":"3ef41c5d-ed87-4503-9f54-ae30f022e98e","resolution":{"observed_at":"2026-08-07T05:14:34.485247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.464034Z","title":"Learning from extrinsic and intrinsic supervisions for domain generalization","venue":null,"work_id":"4d11a69a-7fcd-4fbc-891b-aa83399e7066","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.804727Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:1b172dc5199f283ae46cb6a13968a1153b9871f030a857e34381d450276ab6f5","observation_id":"fc415053-421e-49c3-aa1f-051447aad58e","resolution":{"observed_at":"2026-08-07T05:14:34.468869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.448392Z","title":"Sharpness-aware model-agnostic long-tailed domain generalization","venue":null,"work_id":"f9a11678-4485-499d-a4e6-b1fa1b0b3f97","year":2024},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.808432Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:3e6b6255215692022d11e92e802d2e6e760ba2ea32b35eb5d562dc2055cbc927","observation_id":"22e5d78b-a814-4c13-a669-78815dd068c3","resolution":{"observed_at":"2026-08-07T05:14:34.453537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.432375Z","title":"Unsupervised domain adaptation by backpropagation","venue":null,"work_id":"799c2dfe-adbb-4020-9787-61fa52086f01","year":2015},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.812327Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:ab8da8de8469e7d3e2428a6fdd43846cf63e6674ced05094e720081718edc8a8","observation_id":"46685b49-5f13-4106-8694-67bb794b4d7b","resolution":{"observed_at":"2026-08-07T05:14:34.437434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.414958Z","title":"Sharpness-aware gradient matching for domain generalization","venue":null,"work_id":"b6344ca4-cbbc-4122-a499-725eb9c5f3ba","year":2023},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.816176Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:8cb0144871eca027990ef4be5c95f00dc35729491908abe60f8209ce27b233cc","observation_id":"2f162ac7-b821-4081-b6b7-a28920f7efd8","resolution":{"observed_at":"2026-08-07T05:14:34.420630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.397616Z","title":null,"venue":null,"work_id":"71a14073-7c74-4367-962a-27e602dec027","year":2017},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.820236Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:b2710787c6fb424586777766cf6b71f818a13c400a7acce440af19e7797e651b","observation_id":"deb4f385-a706-4e40-bd71-85b73c7f2fff","resolution":{"observed_at":"2026-08-07T05:14:34.403111Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.382073Z","title":"Deep hashing network for unsupervised domain adaptation","venue":null,"work_id":"012ba9e3-f875-4c49-b616-0fb67c7a005c","year":2017},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.823930Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:66fe58183abd3644fe705f5f2d34b2b61b80d1336403d6eb03f617990b29876a","observation_id":"5dcdfe04-4a61-4efb-9a32-22b4af1ca69a","resolution":{"observed_at":"2026-08-07T05:14:34.387100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.365243Z","title":"Learning to generate novel domains for domain generalization","venue":null,"work_id":"3e093551-557d-46bb-953e-f0606f59f809","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.828024Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:a7371436474bd98d965c54c4919c840bd6d476d484b33e0707cb2bd23a8a2ddf","observation_id":"9921f564-de97-4600-987a-5a271f19d3ed","resolution":{"observed_at":"2026-08-07T05:14:34.371297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.350243Z","title":null,"venue":null,"work_id":"4f325dcf-cad7-44b9-bbb0-055f8917f6a3","year":2019},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.832091Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:c73402bb098bb0bdebab7f6c97a812b92104c904b866080e496d970d376185e7","observation_id":"1a6aead1-a449-48d2-9c27-ceeb09375aa8","resolution":{"observed_at":"2026-08-07T05:14:34.354728Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.334196Z","title":"D’Innocente and B","venue":null,"work_id":"3914ebb4-e12f-412a-8de9-13a6bb22b5cf","year":2018},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.835864Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:e0a71e2bf8ba5e2fbbc5e82ae6155372cf49294086a738ef87b616dbe7ec592d","observation_id":"2ee0b889-5871-4e8d-bd6d-21f32db2763e","resolution":{"observed_at":"2026-08-07T05:14:34.339433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:33.839807Z","title":"Statistical learning theory","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.839807Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:917c6116d761b714e5718ff23ed7db99d6038c874cd5bd9e8603c3c28f02d508","observation_id":"772eecfd-5b78-4c38-b3f1-3eefb962e548","resolution":{"observed_at":"2026-08-07T05:14:33.839807Z","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":"2026-08-07T05:14:34.309685Z","title":"Epi-fcr: Episodic fine-grained cross-domain few-shot learning via feature calibration and relation alignment","venue":null,"work_id":"5ba3ca75-a7a6-44bf-9bce-e5d9cca3a809","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.843553Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:193efc4dd39faf72e411603c3440a04c1abbde2b931523c68ccf342e06d8f88c","observation_id":"0432025f-2ca9-447a-87c9-aca3c2e56441","resolution":{"observed_at":"2026-08-07T05:14:34.314533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.05448","last_updated":"2020-09-11T13:53:56Z","snapshot_observed_at":"2026-07-06T09:54:50.662035Z","submitted_at":"2020-09-11T13:53:56Z","title":"Heterogeneous Domain Generalization via Domain Mixup","version":1},"cited_work":{"arxiv_id":"2009.05448","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.05448","snapshot_observed_at":"2026-08-07T05:14:33.979733Z","title":"Heterogeneous Domain Generalization via Domain Mixup","venue":"cs.CV","work_id":"fadf7e47-66c9-4831-8004-562080ca734c","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.847462Z"},"links":{"cited_paper":"/paper/2009.05448","citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:ad81609b5e1a36ddd0d32f38439769b58db4833ee7cad324ad7e0cbf2cc91885","observation_id":"0c41da8d-6f21-49b8-8d6f-96faf6780fd9","resolution":{"observed_at":"2026-08-07T05:14:33.985515Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.294530Z","title":"Hospedales","venue":null,"work_id":"5687c4de-39b8-416e-9231-82abbf3ebba1","year":2017},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.851888Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:8d243631810a4f23b39b7a0a8c7cf0aaf5e2de3d21ea2e40b7e114aafa5733ee","observation_id":"6b393ac4-b94e-44c1-9008-0b006d4335ad","resolution":{"observed_at":"2026-08-07T05:14:34.299886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.279814Z","title":"Embracing the dark knowledge: Domain generalization using regularized knowledge distillation","venue":null,"work_id":"e67bd0c6-edc4-4a6b-bd1e-3950947559ae","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.855969Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:6556d6b3beecf487bad8158ec468e996f048c98591eb825648bb97a672b3cf88","observation_id":"4f740510-5d49-4c10-8a07-c193ce50e733","resolution":{"observed_at":"2026-08-07T05:14:34.284691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.265007Z","title":"Symmetric self-paced learning for domain generalization","venue":null,"work_id":"9e081ff7-d80d-4d70-84b8-ddd546ce8bda","year":2024},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.860212Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:52df9d3e93ec850f3b6f7f288aa4c9d958039232494f183f8303ac5f6c9f0127","observation_id":"a82e41f5-62f9-4432-b30a-1aff62c846e3","resolution":{"observed_at":"2026-08-07T05:14:34.270457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.249049Z","title":"Domain generalization via entropy regularization","venue":null,"work_id":"5a289814-b7dd-44a3-b1cc-14bcc959f79f","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.864540Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:7c78abe45eab9e1274d959b64c51150e4beaa58694ac0b69c0cbb43f14535c15","observation_id":"e3af3806-1fac-4c97-8cd8-d09254317850","resolution":{"observed_at":"2026-08-07T05:14:34.254493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.234742Z","title":"Deep domain-adversarial image generation for domain generalisation","venue":null,"work_id":"a04e2c3c-c258-48a2-b399-8c77f0451e14","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.868566Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:52743b2102f9c074433ced7f3962ce442a9378652f909d2589f4c6c434220dbf","observation_id":"a15aae32-ee7c-4c3b-8822-a565ff1076c4","resolution":{"observed_at":"2026-08-07T05:14:34.239549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.220632Z","title":"A sentence speaks a thousand images: Domain generalization through distilling clip with language guidance","venue":null,"work_id":"35c537bb-f5e3-4e10-bb51-2aba84342955","year":2023},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.872156Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:402bcb7cca4ec61dd592187294edb9a98be0a96b791b290db45828e4cec3f252","observation_id":"81a27670-fa20-4c41-973a-9c0f3d9416df","resolution":{"observed_at":"2026-08-07T05:14:34.225322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.205245Z","title":"Self-challenging improves cross-domain generalization","venue":null,"work_id":"c53efc96-1a5b-40fe-a7f2-bd4ea3195174","year":2020},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.875908Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:7377ec9595e50e53f2638c0f2cc61342a164a7531a8a7426f7a85f1d62291852","observation_id":"705798bf-813f-409c-b95f-f77d858cf6a9","resolution":{"observed_at":"2026-08-07T05:14:34.210556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.190089Z","title":"A fourier-based framework for domain generalization","venue":null,"work_id":"1d547f81-c134-4e3a-a742-97820bcba555","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.879845Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:f085083f540e34ba170b1c72e191d05e16af92c181dffe214cb0920201be727a","observation_id":"121d3a4a-1496-4d65-8b01-a60b25ac8434","resolution":{"observed_at":"2026-08-07T05:14:34.195363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.173564Z","title":"Domain generalization using causal matching","venue":null,"work_id":"ee13894a-2e0e-41ba-a806-569148e902d0","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.884155Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:8eed6399103171c61c44a86bcc0433123a4a8863577ddd6b88b1a5855d5161d2","observation_id":"1d578ede-5da4-4d46-a712-b6a3afad2cf8","resolution":{"observed_at":"2026-08-07T05:14:34.179536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.157456Z","title":"Feature stylization and domain-aware contrastive learning for domain generalization","venue":null,"work_id":"ac62df48-219a-4204-a95d-032d255a86e0","year":2021},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.888143Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:4400fd9bd39c254cd160b529055f49bc827225793a5a1073c5cdab709d51f32b","observation_id":"ae645b15-f1e4-4002-9823-c88610f2eca6","resolution":{"observed_at":"2026-08-07T05:14:34.162374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.140453Z","title":"Domain generalization via frequency-domain-based feature disentanglement and interaction","venue":null,"work_id":"2df23ec8-e7c7-4f81-9d7c-445319ea566c","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.892076Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:5b659c4fdb2c78daa85f3aff26ee2a4b119be0f29a6dcd70bad7cc12f21a61a7","observation_id":"2941771e-6d2e-4ea4-998a-669e111754d1","resolution":{"observed_at":"2026-08-07T05:14:34.146669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.123813Z","title":"Pcl: Proxy- based contrastive learning for domain generalization","venue":null,"work_id":"c468133e-3457-43bc-a216-4e105e793685","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.896000Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:e74cc03b0166dff072fc2d41bdfb721578f9117eec1f8785ef20fb84305099d0","observation_id":"82655380-a728-4780-8b85-f4dcab1e3594","resolution":{"observed_at":"2026-08-07T05:14:34.129116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.107304Z","title":"Style neophile: Constantly seeking novel styles for domain generalization","venue":null,"work_id":"610bcfc6-9ab7-4911-851e-1248309ec23f","year":2022},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.900178Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:6bf7bbe92a852ac53b48268d5a5716e43a6cb106e99cc935209ae6448f407fa8","observation_id":"3dc4ab31-a8e1-412e-adf6-c7da7fb3d710","resolution":{"observed_at":"2026-08-07T05:14:34.113355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.089679Z","title":"Improving generalization with domain convex game","venue":null,"work_id":"9b7122f2-319a-4209-8ffa-e16dc2d105a9","year":2023},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.904504Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:4aeb1ded569c89250c8f0f9cb226494a1bb37d3ae513a2c0e6d2d44e182d6d9e","observation_id":"5df3bf79-52b5-42e5-8d02-dd80e0265a00","resolution":{"observed_at":"2026-08-07T05:14:34.095730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.074177Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"bfddd6da-67f8-4b9e-998f-55a0b5f21c4b","year":2015},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.909201Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:51fcc9f5a979ce8422118ea981cf625921978900c2c2f0bd8967c9a4527bb972","observation_id":"2035e985-53d2-4a44-b5e7-3f7d37af44db","resolution":{"observed_at":"2026-08-07T05:14:34.079327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:34.058245Z","title":null,"venue":null,"work_id":"03708072-df71-4960-9fd9-a2df69d19c24","year":2009},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.914048Z"},"links":{"citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:8372abb459ef2274b83d8dfdcf78e1e04d80efb588cf041b090f940656e03710","observation_id":"9ad99fd5-1ac7-43db-968f-3da2e5d6b479","resolution":{"observed_at":"2026-08-07T05:14:34.063536Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12530","last_updated":"2023-09-21T23:06:19Z","snapshot_observed_at":"2026-07-06T16:22:09.775204Z","submitted_at":"2023-09-21T23:06:19Z","title":"A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance","version":1},"cited_work":{"arxiv_id":"2309.12530","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.12530","snapshot_observed_at":"2026-08-07T05:14:33.955499Z","title":"A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance","venue":"cs.CV","work_id":"d88a8b41-0b5a-4db3-b030-6834bea131cd","year":2023},"citing_paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:33.918662Z"},"links":{"cited_paper":"/paper/2309.12530","citing_paper":"/paper/2506.08518"},"observation_digest":"sha256:97430600420656fc5a6cf3a80c1471a74e92716b9f34add0b29bb3d159bb7db2","observation_id":"94863eb9-73ad-4d8f-b2c3-0079878c0b36","resolution":{"observed_at":"2026-08-07T05:14:33.963512Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.08518","last_updated":"2025-06-10T07:36:40Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T05:05:58.499295Z","submitted_at":"2025-06-10T07:36:40Z","title":"FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":46},"total_outbound_references":60},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2506.08518."}