{"as_of":"2026-08-08T20:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:add77b5fec4c12d156ff5cdd2a35d60d04e16a265bd278aad647bd68c71dc830","coverage":[{"denominator":100,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:58:44.400023Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2506.00969/citation-record","integrity":"/paper/2506.00969/integrity","json":"/paper/2506.00969/citation-record.json","paper":"/paper/2506.00969"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:58:31.860484Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:31.860484Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:c25b5d32e2a48eea55e12dac22007156b43356f41d563a0b515be6c3dc72d967","observation_id":"d0104d3f-4806-44ff-8e14-93c13da261f7","resolution":{"observed_at":"2026-08-07T11:58:31.860484Z","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":"2026-08-07T11:58:31.933275Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:31.933275Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:c4dbdff73adaf3ef2adc8c07afc22340529644a888bf208d3529af5b71373978","observation_id":"5f881848-35e6-488e-8fc5-84b61c1c4306","resolution":{"observed_at":"2026-08-07T11:58:31.933275Z","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":"2026-08-07T11:58:32.166350Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.166350Z"},"links":{"cited_paper":"/paper/1907.02893","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9d5c80854dfe03931e029b3f37661fcc94bc44dede94b054e2db4fe8c3baa0ff","observation_id":"214a915e-ae86-40af-a0c2-8cf8d28c2a4b","resolution":{"observed_at":"2026-08-07T11:58:32.166350Z","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":"2026-08-07T11:58:32.287508Z","title":"2007.Stochastic simulation: algorithms and analysis","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.287508Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:30223ec4ebeb7b84b1d9c8d5aa860a4b8646a943910ce8ffa47f36e14b10f56a","observation_id":"8913aba0-4c58-4cba-86ce-8bd0504696d3","resolution":{"observed_at":"2026-08-07T11:58:32.287508Z","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":"2026-08-07T11:58:32.412747Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.412747Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:8af9718a8a1ac8206ddd8c83a684656cbbd19302af76786e9b9934ff07368f9f","observation_id":"5767d15c-bdbd-4247-aa23-2936cdf30e54","resolution":{"observed_at":"2026-08-07T11:58:32.412747Z","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":"2026-08-07T11:58:32.533297Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.533297Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:77f654d7a91a3091b3f90ccca20bfb9314a709bf64eec33e55cfb312153c92cf","observation_id":"b5c4a902-5d52-4f00-8a85-df456b4182c6","resolution":{"observed_at":"2026-08-07T11:58:32.533297Z","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":"2026-08-07T11:58:32.750552Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.750552Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:6376bad6dc450560b8ceb04deb8bd8534bb2143c81197c199964c70863a603c4","observation_id":"45890777-ec42-4bf7-a9c1-07bc6023ac56","resolution":{"observed_at":"2026-08-07T11:58:32.750552Z","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":"2026-08-07T11:58:32.619701Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.619701Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:ae4852a6bfc8a372272f60a19d742a4acf3c2380a7d08d798c186d4dc8b3c778","observation_id":"c3a494cc-4ac1-44eb-a1e7-ab5aabce8651","resolution":{"observed_at":"2026-08-07T11:58:32.619701Z","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":"2026-08-07T11:58:32.994675Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.994675Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9c9c682b452638bdb41049c962a7b097890017c2ad0e2208ae241b1f843e1851","observation_id":"11a02144-cedc-48c5-a092-df859acf1f35","resolution":{"observed_at":"2026-08-07T11:58:32.994675Z","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":"2026-08-07T11:58:32.855801Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.855801Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:760c1ab85bc0e1963922880531c56a60e90a26eee71796fca511fbcd734404c6","observation_id":"3686a796-6fde-48fe-bdb3-cd9db83efbb1","resolution":{"observed_at":"2026-08-07T11:58:32.855801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.02011","last_updated":"2023-07-10T20:46:14Z","snapshot_observed_at":"2026-08-07T19:54:44.992898Z","submitted_at":"2023-03-03T15:27:16Z","title":"Diagnosing Model Performance Under Distribution Shift","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.02011","snapshot_observed_at":"2026-08-07T11:58:33.223518Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.223518Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:2f040381a9463e6ea460ee59be12124508077677bf09bf6899c3a938baedbd88","observation_id":"2f115867-2166-40e9-a734-5b4d4805b3e6","resolution":{"observed_at":"2026-08-07T11:58:33.223518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14655","last_updated":"2024-01-26T05:28:17Z","snapshot_observed_at":"2026-07-06T17:20:45.227692Z","submitted_at":"2024-01-26T05:28:17Z","title":"Distributionally Robust Optimization and Robust Statistics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14655","snapshot_observed_at":"2026-08-07T11:58:33.118703Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.118703Z"},"links":{"cited_paper":"/paper/2401.14655","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9567b2905bca0d187923f15c0dd9484661ec7e3b9c20c66406d574727561cc95","observation_id":"06c42f36-c6a8-4fa2-9197-b5257f6a99b0","resolution":{"observed_at":"2026-08-07T11:58:33.118703Z","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":"2026-08-07T11:58:33.522228Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.522228Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:7dca844a6603646051c2637d796ebc5ae6c083da7b6333bd6d5f7cbad60d95b0","observation_id":"3ba0adcc-6cbe-4573-8090-28e24ee04472","resolution":{"observed_at":"2026-08-07T11:58:33.522228Z","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":"2026-08-07T11:58:33.385791Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.385791Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:bfcbd58e6342f92b48c69bfdde48ea1e1cf6684d8a629cc9c73c5fe694e9b449","observation_id":"dd21c37d-5323-424e-b21f-f2413bf51926","resolution":{"observed_at":"2026-08-07T11:58:33.385791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07766","last_updated":"2023-03-02T09:43:58Z","snapshot_observed_at":"2026-08-01T23:20:02.599492Z","submitted_at":"2022-06-15T19:04:02Z","title":"Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization","version":2},"cited_work":{"arxiv_id":"2206.07766","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.07766","snapshot_observed_at":"2026-08-07T11:58:45.045106Z","title":"Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization","venue":"cs.LG","work_id":"868e8dd4-d2d7-4d23-9a09-0fbc1d0478b1","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.743063Z"},"links":{"cited_paper":"/paper/2206.07766","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:e0828c718f6bfb65b7926bde00903436140c4b64af32ae0ef69f761ebba04203","observation_id":"9e00c426-979d-45d4-878d-73620d0f4f66","resolution":{"observed_at":"2026-08-07T11:58:45.179184Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:33.640475Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.640475Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:5c8eeaa52cd1828287beaf87236fa234fd32151723f13938388f294fc519999b","observation_id":"ce24afac-ce51-4823-96fe-f53700ff4f15","resolution":{"observed_at":"2026-08-07T11:58:33.640475Z","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":"2026-08-07T11:58:34.008289Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.008289Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:152b697c737a486ad26ccf1873c3299589c12a9f726112f275899966c2368d0b","observation_id":"da9b579d-1255-4718-9fae-2e39cbee312d","resolution":{"observed_at":"2026-08-07T11:58:34.008289Z","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":"2026-08-07T11:58:33.890039Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:33.890039Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:6b4367c62afe1670d7e624c1eab1bc2b356b7d8acc4e7f6676059675a04c6543","observation_id":"5b20ae78-be9d-4f32-b2f6-a015833cf678","resolution":{"observed_at":"2026-08-07T11:58:33.890039Z","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":"2026-08-07T11:58:34.240916Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.240916Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:ad118d33810374ef8fa76faf04dd0a9950151516ad2e53a1945fae0904f92951","observation_id":"81c0a724-519b-4467-a3a4-4538e9c56d3d","resolution":{"observed_at":"2026-08-07T11:58:34.240916Z","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":"2026-08-07T11:58:34.130117Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.130117Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:21ca0bff0f648cc900b623e94deff93ee77ee1ddeb50a96363207f86a2a8089d","observation_id":"54048ab5-3e64-48d6-a591-51aed8dfe897","resolution":{"observed_at":"2026-08-07T11:58:34.130117Z","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":"2026-08-07T11:58:34.505352Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.505352Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:2c87743daffee83081f93b031a2f493ed1c34c00ce4daaee99c8aa34fb0434f2","observation_id":"4f6e134f-8dac-4b7d-bf97-0be64407f088","resolution":{"observed_at":"2026-08-07T11:58:34.505352Z","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":"2026-08-07T11:58:34.396180Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.396180Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:873e1e744fba7989a930514b393c183d12f1dee17b59883f50b9220c055ccac0","observation_id":"d0789814-c495-4e0f-90cd-186515d965f8","resolution":{"observed_at":"2026-08-07T11:58:34.396180Z","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":"2026-08-07T11:58:34.780976Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.780976Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:e6aed3134e62a56fcddd2fcafeaf0e76c63f37015a6378ea4a93e9fbd273bd13","observation_id":"b7d37b22-0f5b-4100-b10d-88f3fa8ffb55","resolution":{"observed_at":"2026-08-07T11:58:34.780976Z","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":"2026-08-07T11:58:34.644458Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.644458Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:b202b27a4c105c1e4c33617ede52e314a13255d15ec6a706f8f37d938f218b86","observation_id":"78cca9db-efa9-4ed5-87b1-1c00d9678607","resolution":{"observed_at":"2026-08-07T11:58:34.644458Z","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":"2026-08-07T11:58:58.936220Z","title":null,"venue":null,"work_id":"3c7da26b-13c7-4b24-b920-a65fe4940d62","year":2014},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.040172Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:c27f16e84160aaba72c46e47518223f2b9968916e8685102cda44a404123c7c9","observation_id":"1c4f5938-72e6-41d8-93dc-6421021b5cbe","resolution":{"observed_at":"2026-08-07T11:58:59.046606Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:34.904920Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:34.904920Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:cf1202a2e8b0d9bb1ad954c0d81f1c732602a9516ea90aae849e712b9ddea0c3","observation_id":"89060285-6785-439c-be89-db9aa1368245","resolution":{"observed_at":"2026-08-07T11:58:34.904920Z","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":"2026-08-07T11:58:58.441231Z","title":null,"venue":null,"work_id":"342cbd9f-795b-4e3d-aafc-e974ac0ebc59","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.396226Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:f4fe05fde26fb83c256af1e1a762bb4ce8826c3e73155aa39eded3eef697da17","observation_id":"05706431-eb59-474d-a59e-e70f99da3586","resolution":{"observed_at":"2026-08-07T11:58:58.536270Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:58.687915Z","title":null,"venue":null,"work_id":"6fdba035-9f61-488d-929f-0e0b4b71ef57","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.198154Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:de6a63483a7c15568e242c9c2d21d31f2dfa5ef5f41885f1f3497d1fb6e6b3b8","observation_id":"acfbd952-4100-4250-b39e-6e859a94fbe2","resolution":{"observed_at":"2026-08-07T11:58:58.822485Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.12703","last_updated":"2023-04-17T15:54:03Z","snapshot_observed_at":"2026-07-06T14:22:00.790764Z","submitted_at":"2022-11-23T04:49:18Z","title":"Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.12703","snapshot_observed_at":"2026-08-07T11:58:35.639346Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.639346Z"},"links":{"cited_paper":"/paper/2211.12703","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:2aba7548c5e1b65c99259d36062b48c8c094bef50748b0e22ac195b10c0ba563","observation_id":"de9cac41-5087-4d85-a730-e9def9a7598e","resolution":{"observed_at":"2026-08-07T11:58:35.639346Z","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":"2026-08-07T11:58:58.203597Z","title":null,"venue":null,"work_id":"92828f00-415d-413a-a981-a5d201bac34b","year":2015},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.484777Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:95645cdaa55b9a0e63257be0b46ab66b33088a059cb260297607a9a126351889","observation_id":"0a867302-78d8-4880-ba08-5cecf3659234","resolution":{"observed_at":"2026-08-07T11:58:58.306589Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03296","last_updated":"2023-08-07T04:47:42Z","snapshot_observed_at":"2026-07-06T16:03:14.694457Z","submitted_at":"2023-08-07T04:47:42Z","title":"Studying Large Language Model Generalization with Influence Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03296","snapshot_observed_at":"2026-08-07T11:58:35.947953Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.947953Z"},"links":{"cited_paper":"/paper/2308.03296","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:86c1bdac1e2b05c1d13aa895b43927010b20e00cef2bfee1b34eb0f9f6963ec0","observation_id":"49668fd1-1a9f-4bed-8f6f-dfe34b11ccdc","resolution":{"observed_at":"2026-08-07T11:58:35.947953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.07832","last_updated":"2026-08-05T19:09:17Z","snapshot_observed_at":"2026-08-08T20:13:59.147041Z","submitted_at":"2024-07-31T14:49:35Z","title":"LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers","version":13},"cited_work":{"arxiv_id":"2408.07832","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.07832","snapshot_observed_at":"2026-08-07T11:58:44.702237Z","title":"LADDER: Language-Driven Slice Discovery and Error Rectification in Vision Classifiers","venue":"cs.CL","work_id":"6e8e9412-12ec-4d8d-b5d5-eb7828944390","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:35.805192Z"},"links":{"cited_paper":"/paper/2408.07832","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9db0448666a6d14551b7248a23796987e68f3b1129fdc9aa8214090501a4056a","observation_id":"4020116e-803d-4dc8-8ad0-9c3daed14a8e","resolution":{"observed_at":"2026-08-07T11:58:44.854896Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.01434","last_updated":"2020-07-02T23:08:07Z","snapshot_observed_at":"2026-08-07T18:31:23.290877Z","submitted_at":"2020-07-02T23:08:07Z","title":"In Search of Lost Domain Generalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.01434","snapshot_observed_at":"2026-08-07T11:58:36.140893Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.140893Z"},"links":{"cited_paper":"/paper/2007.01434","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:c796f7d6866894a9c282be72757bbdbe8b6cd88e7f52d042aaec104a5c1b5e86","observation_id":"bd9987cd-6c09-4ab1-93d0-268902378cf1","resolution":{"observed_at":"2026-08-07T11:58:36.140893Z","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":"2026-08-07T11:58:58.000501Z","title":null,"venue":null,"work_id":"fdbf7275-54b3-4fe3-8984-7ebe7503252e","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.052124Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:974b01043de45caed404b93493b1e03a21a96f499a79d87ec0ee341be098a250","observation_id":"af50a8ae-d17b-421c-a1d5-863fcede0d5e","resolution":{"observed_at":"2026-08-07T11:58:58.085920Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:57.731657Z","title":null,"venue":null,"work_id":"efa1e9e9-211d-420c-bda1-6f1a3b373e9a","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.378500Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:49efcaf6787c241638e538b4859fb75282f7e5b99134cfe7ad79dcede939f695","observation_id":"484c78bf-18f4-4bd5-89da-6458ac0ef53b","resolution":{"observed_at":"2026-08-07T11:58:57.846932Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:36.279212Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.279212Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:81c37f099101615933f8bc7a7decaeee76f475520e4e053c54d08d2245d0bd10","observation_id":"1ec14c70-21f2-4545-93e9-7e7debb8ca9a","resolution":{"observed_at":"2026-08-07T11:58:36.279212Z","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":"2026-08-07T11:58:57.242573Z","title":null,"venue":null,"work_id":"f2d745af-8ba2-4f89-a91e-65a8b68d28d7","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.612365Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:196253d9ec54834e9bb96d5b64538e17ee6c5ae4c2a24dd34f8373c2b9afe73e","observation_id":"be74b717-58cb-4477-bc62-983e0990de57","resolution":{"observed_at":"2026-08-07T11:58:57.350197Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:57.477181Z","title":null,"venue":null,"work_id":"3ce17f81-302d-471e-ae67-4152ef7200c3","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.483294Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:f5aeca050b470a3e53dae222a5c33e233bad2fd4325325c1654fe60ab1164c8a","observation_id":"ed752186-6e41-4c64-a4ed-ae735da45922","resolution":{"observed_at":"2026-08-07T11:58:57.586127Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:37.023994Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.023994Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:ddf539a2573cc367df86bf8e5ec8c896f011cb1d3c909e3d63507f806b699ea6","observation_id":"e7af7597-52a9-42f6-8385-baf447b3a807","resolution":{"observed_at":"2026-08-07T11:58:37.023994Z","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":"2026-08-07T11:58:37.136887Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.136887Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:879f0ed7f3b10711160458be45b682cb960ba6fd0a0dc571ee3ee0985b2643b2","observation_id":"284b045a-4e79-4714-88a4-e369e4a0f120","resolution":{"observed_at":"2026-08-07T11:58:37.136887Z","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":"2026-08-07T11:58:56.771905Z","title":null,"venue":null,"work_id":"31552ff3-e025-4bfc-b49f-bc3b022f9cab","year":2018},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.882523Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:3cdd6a3bb2e3f9b473da837e12c393669cf733a45ee5ec49d86015c23d2efd95","observation_id":"bd980eab-be96-49b4-b918-5ce12d052e80","resolution":{"observed_at":"2026-08-07T11:58:56.875739Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:56.254039Z","title":null,"venue":null,"work_id":"8f9b4d54-4198-4c1c-88a1-16fd7922cf48","year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.421447Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:dd06996bad43792712d7063b118d13b69c684e7fec2549d7151d067550d8db76","observation_id":"7e88d332-58b8-4f25-a7b5-94173d9670cb","resolution":{"observed_at":"2026-08-07T11:58:56.383197Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:37.521381Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.521381Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:2878cf9d8f956caff0132e9a2b8251b73e25f357f373617e41637a80c8ed55de","observation_id":"3e891025-1ed6-4357-8d33-fa1a68cd0769","resolution":{"observed_at":"2026-08-07T11:58:37.521381Z","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":"2026-08-07T11:58:56.530683Z","title":null,"venue":null,"work_id":"ce22f9e5-13ba-4cfb-8bb5-3c1a6d3c8d49","year":2017},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.269632Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:292ddff60704b330ad74c71b45b4fd927d5b11731fffd5c87be967a218c97131","observation_id":"d0d3168a-6062-4351-9539-1e35d08cc94b","resolution":{"observed_at":"2026-08-07T11:58:56.633919Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:55.687982Z","title":null,"venue":null,"work_id":"173034c8-2afd-4933-96cb-f4db013472c5","year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.732374Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9c1aa4e1644239bb9e4042a68486718e26ef4670d30fcfc92fabb3e483317a35","observation_id":"7ce5e286-539a-4ac9-b5e1-f9520655feba","resolution":{"observed_at":"2026-08-07T11:58:55.850046Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:55.485560Z","title":null,"venue":null,"work_id":"bd67180e-0672-44e3-9a34-a4cc429f4d5d","year":2025},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.842390Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:dc062f639496e4a555f68c91dc185214859b71735f59cb6f1e99e2ace34c07a3","observation_id":"7f80c2f0-c58e-4d06-818c-c45d1897e9e7","resolution":{"observed_at":"2026-08-07T11:58:55.579854Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:55.975520Z","title":null,"venue":null,"work_id":"a2f3e266-b915-4f66-999d-8d2bdee1dfa5","year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.623123Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:ed92ab6518724efe335d596e8ee608a6d81ea72715c9a968d45c1e9d94f05429","observation_id":"64d876f6-8974-4509-a0ed-c00f48cc509e","resolution":{"observed_at":"2026-08-07T11:58:56.107735Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:55.025380Z","title":null,"venue":null,"work_id":"cbd5476f-c213-4369-8951-19d24fea74bc","year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.046739Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:51ee7cfecae188241c4c9ef734d87cb96b4f0246e5f8784b7725f5d825cf0139","observation_id":"f135112d-7fc3-44d4-935d-e957804b1ccd","resolution":{"observed_at":"2026-08-07T11:58:55.110734Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:54.743604Z","title":null,"venue":null,"work_id":"bc636faa-282b-4eed-8f26-dc3f96007f81","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.209007Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:6c56e11957ed97aa642b96d40268f78c51361e9fe665247857c6e216c97e3347","observation_id":"9361f834-f87e-4e90-814e-7c82437f690c","resolution":{"observed_at":"2026-08-07T11:58:54.867250Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:55.263788Z","title":null,"venue":null,"work_id":"055374cb-bcc7-4836-8753-65bc95d3d695","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:37.923928Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:f4411a8eb989f43d42b17b98a07a6e7c1bea9aa6dc4352bdb8962d309b308dbe","observation_id":"db951274-46e6-4eca-803c-8655486c2afe","resolution":{"observed_at":"2026-08-07T11:58:55.340079Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:54.344630Z","title":null,"venue":null,"work_id":"98bfe38a-c599-42a7-97ab-68823f908036","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.589416Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:8bc7d506e5abdbbb7e0425458cd27995b607d3ff7274953eeb2ee6c2b6f67e9c","observation_id":"d74e2928-6ac5-4148-a8ac-1a32ef0c944e","resolution":{"observed_at":"2026-08-07T11:58:54.456389Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:54.025693Z","title":null,"venue":null,"work_id":"26f21577-2fb6-4a27-afd6-596e5c41cf58","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.724065Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:f5cdc8a25f3d6c3b775877f5237a6c8145c0db887dd17847a029166543ec26f2","observation_id":"7780b25f-1d21-460c-b592-6154f7f41997","resolution":{"observed_at":"2026-08-07T11:58:54.196721Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:54.532990Z","title":null,"venue":null,"work_id":"54399f4f-77c8-4b66-bdd7-97b373a6dcb3","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.357482Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:aab95a0a6b5a073d6b5864e82fb14df7c4781d9c5c4049832c56c843c3afae86","observation_id":"5ea280d8-5fff-43a6-94b5-d15e243d998c","resolution":{"observed_at":"2026-08-07T11:58:54.620780Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:53.607945Z","title":null,"venue":null,"work_id":"e30e2146-4196-413f-822c-78cce45fdc3f","year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.971005Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:028c0fb277c8ebc47d517b0b5972ea2f8ca18603e796bc254b9cb0823bc7516b","observation_id":"db442fe1-6f3c-426a-a0c5-35fbd7be84ed","resolution":{"observed_at":"2026-08-07T11:58:53.685837Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:53.353100Z","title":null,"venue":null,"work_id":"8069d796-1def-4416-88e1-1a054885e1a7","year":2008},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.102584Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:fbfb17a9bdbd1bb4609715d0a51cacfdfd13a22e657a351d69b65bfacfcc0a5c","observation_id":"b96e90e1-5077-462c-97b5-4b360f8606fb","resolution":{"observed_at":"2026-08-07T11:58:53.518488Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:53.060655Z","title":null,"venue":null,"work_id":"4239c6b2-3f7d-41d3-8815-e3eff85066b7","year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.203044Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:88056a464f100a3faf7da35d367ae4c6ac6dcc66ae744d760bb2f4acc8bf87ab","observation_id":"24386603-55a4-423f-86fc-a2e44c4a179f","resolution":{"observed_at":"2026-08-07T11:58:53.247405Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:53.796513Z","title":null,"venue":null,"work_id":"3213bbd7-6dbf-421f-ab4d-48640c89ad70","year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.829400Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:2cb35f250d6f3f8653bb4165a380ae63ed2936423de4acdb63e4e3649505c63e","observation_id":"30a439cd-efe4-4ea6-b635-f027cbf53eab","resolution":{"observed_at":"2026-08-07T11:58:53.920608Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:39.441458Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.441458Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:60ca2aaa2e3348cce2ea0211aefd3ea8f6e2bb6f970c50cfdf0f39749babed71","observation_id":"4c375d4a-6c25-4c06-8692-6e2cd07d322e","resolution":{"observed_at":"2026-08-07T11:58:39.441458Z","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":"2026-08-07T11:58:52.570616Z","title":null,"venue":null,"work_id":"d42026a5-5d61-4f9b-8a2c-1a66d25c79c5","year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.562253Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:11c2551aa3646a4c32fc23bd038f0d1a2668fded8ff49ee47fc92f28ff7a8848","observation_id":"de9424ce-36bc-41f2-935e-708b55995235","resolution":{"observed_at":"2026-08-07T11:58:52.684287Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:52.384567Z","title":null,"venue":null,"work_id":"52c93412-40d8-47d3-8a99-9ff0c4e0da7c","year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.632092Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:5f7739c83ccab5ce534118ac21a34628b3961a952ebb0bf9caaafc930b2983ed","observation_id":"08cecb38-4f4b-4b67-924e-01ef3a4da577","resolution":{"observed_at":"2026-08-07T11:58:52.471013Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:52.786164Z","title":null,"venue":null,"work_id":"c9c1db2a-1376-4af8-bae6-d4197e95c019","year":2018},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.316579Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:2458cbf57d3ec50499306751329d725348ab44bb7a2dbe7daef415270b677fb4","observation_id":"23290181-bd37-4958-a12d-c1d5ccedfe52","resolution":{"observed_at":"2026-08-07T11:58:52.907746Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:39.923093Z","title":"Everyone wants to do the model work, not the data work","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.923093Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:737d5a58c781475baf76ca7967843088ea7a15769c2a469c4b8e3e613735a122","observation_id":"c0aeb92f-e01d-42f4-b44a-27aaaa675b7e","resolution":{"observed_at":"2026-08-07T11:58:39.923093Z","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":"2026-08-07T11:58:52.155443Z","title":null,"venue":null,"work_id":"92e8391e-606b-4958-8ba0-7132938e4fc4","year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.051954Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:e53b54e92ccc12446acfd18a533ec138a6c33e83282eef3434053796dab52ef7","observation_id":"2ef64c62-e1b2-4c1d-9d1f-26dc123307fd","resolution":{"observed_at":"2026-08-07T11:58:52.286135Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:51.956995Z","title":null,"venue":null,"work_id":"58b0facd-5699-4c2f-a27f-25243757ea14","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.153339Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:380846f7c42b2a6e6d29822c548066f41da50cf3c1b1a755bc233848c71782eb","observation_id":"8898d1f4-df71-47f2-b662-eda4060dfb9d","resolution":{"observed_at":"2026-08-07T11:58:52.056734Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T11:58:39.775596Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:39.775596Z"},"links":{"cited_paper":"/paper/1911.08731","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:05862ea15988a3c7437fa22c1c0b495f7e2689fea5e5e4798240269d95430081","observation_id":"1d3cca8d-14fe-4298-a039-ee427160b420","resolution":{"observed_at":"2026-08-07T11:58:39.775596Z","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":"2026-08-07T11:58:51.161218Z","title":null,"venue":null,"work_id":"13756f3b-a6d3-47f2-a3cf-1f1ed7b5596e","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.451547Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:59a80c08ba33e753869a4a7670212cf42abc2367063d83b8bf7da8a16451bf55","observation_id":"d59054af-291e-4973-8aae-0c9a1063a46d","resolution":{"observed_at":"2026-08-07T11:58:51.277236Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:50.898860Z","title":null,"venue":null,"work_id":"213d4ce7-5320-42c9-883c-0521141afb45","year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.561266Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:500759dafe015705981ae1ba5ff9d44ef9a7ae46ec5dabcda353f6df53a7e9c4","observation_id":"434d22de-cea4-48af-92a3-5d73238e98da","resolution":{"observed_at":"2026-08-07T11:58:51.042138Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.13624","last_updated":"2023-07-27T13:13:11Z","snapshot_observed_at":"2026-08-08T15:51:26.409443Z","submitted_at":"2021-08-31T05:28:42Z","title":"Towards Out-Of-Distribution Generalization: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.13624","snapshot_observed_at":"2026-08-07T11:58:40.683751Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.683751Z"},"links":{"cited_paper":"/paper/2108.13624","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:10bc97c8eaeedc0fb539382c98871c1441df8a6a827b162d6b52a3e40cb047c0","observation_id":"1d23353b-4828-4931-8735-97179e77937a","resolution":{"observed_at":"2026-08-07T11:58:40.683751Z","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":"2026-08-07T11:58:51.718465Z","title":null,"venue":null,"work_id":"94a601b6-4aee-4d96-a1b1-33313f339008","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.258751Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:67c8f589b22d376b4ca081d4e8a9644d1cdec709027c1a096e0ba48ffd611c85","observation_id":"4543f4ef-5e25-42fd-bac8-0fb827dd2122","resolution":{"observed_at":"2026-08-07T11:58:51.838544Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:51.428048Z","title":"Advances in Neural Information Processing Systems36 (2023), 74995–75008","venue":null,"work_id":"826d0e4f-e19b-4b29-9410-9d4a09b86340","year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.358224Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:d3197fd23993bd5d0561d5b0c5bcc5d27816dfda1453b17591dde2f26078cb9f","observation_id":"5d058e22-f3ae-4ea4-9a2c-b971da7073ee","resolution":{"observed_at":"2026-08-07T11:58:51.581939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:50.229591Z","title":null,"venue":null,"work_id":"4a655dc9-5701-44d8-b2df-af9d74b858dc","year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.157870Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:e44aff9adb6774183cf472f8b4faba3ea9d9ad2807f8b88eb76189aa89849fe5","observation_id":"9f321146-fddf-4cbc-9344-c385ec9015e9","resolution":{"observed_at":"2026-08-07T11:58:50.355170Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:49.990318Z","title":"Smith, and Yejin Choi","venue":null,"work_id":"2314d686-212e-4052-a257-3d3a72fd008b","year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.275513Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:697af42f5604f68b9689f876ea54c5568b8e167d28dfc6cd738a10c5a668719b","observation_id":"07c772ed-18b7-4d62-9e12-3783fd43dfc7","resolution":{"observed_at":"2026-08-07T11:58:50.105126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:49.694930Z","title":null,"venue":null,"work_id":"c6b5b38a-7d96-4333-af05-0fe5701bb0dc","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.386007Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:113ff79f956b1a3895d682569754c14915f89e7b16542946f8d886d02d67a10d","observation_id":"04446bb4-aae6-4b0b-b3be-0b348886ff08","resolution":{"observed_at":"2026-08-07T11:58:49.864291Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:40.810844Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.810844Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:f42b6bbec9fc86c3b6a7be54cf9f49641f4d37fb578e36ee6acc4954c7391c76","observation_id":"2bbf876c-8e61-445f-bb84-955b82745641","resolution":{"observed_at":"2026-08-07T11:58:40.810844Z","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":"2026-08-07T11:58:50.648216Z","title":null,"venue":null,"work_id":"c0a6f26b-11b3-431d-9185-b3af8381467c","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:40.923807Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:fb70aa96e68f8c653418722aefeb1cc0b196a3d6053be50a34ef8c15cbd0de1f","observation_id":"980acc25-9eee-47a2-897c-5481075670e5","resolution":{"observed_at":"2026-08-07T11:58:50.749606Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:48.966709Z","title":null,"venue":null,"work_id":"baecacd5-0758-466b-8433-f29fd7ce37f0","year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.805515Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:abc395d9e021b6a23fc21e29b4ea9d842978a34116618b9ec203941f0474284b","observation_id":"58fe90d9-7e3d-49b6-9778-9afe382bdced","resolution":{"observed_at":"2026-08-07T11:58:49.054569Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:41.929105Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.929105Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:055f8caaad2cc8064749e3b621e208d7338e3324382597083c75290d73ad16a0","observation_id":"4bad5b33-baec-49eb-bc37-5d16a793fcf7","resolution":{"observed_at":"2026-08-07T11:58:41.929105Z","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":"2026-08-07T11:58:42.065835Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.065835Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:ad02b5e2d4f927e3af250e4c51050296e5d59b52016ed322b5d2f038d9252fc7","observation_id":"d801b395-09bb-4a48-a282-409b0711c865","resolution":{"observed_at":"2026-08-07T11:58:42.065835Z","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":"2026-08-07T11:58:48.669503Z","title":null,"venue":null,"work_id":"5f369a92-0d63-47e9-99b0-3972706bf0e5","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.240608Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:0c9eb396f2918bf501d5d33146c6d7de638dfdf868a1650922de81220f015c75","observation_id":"7826b880-d9ed-4624-a190-c775ea8b4284","resolution":{"observed_at":"2026-08-07T11:58:48.809941Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:49.429359Z","title":"Tipton, J","venue":null,"work_id":"5a1eb4ba-e302-457d-8085-5602c5aba931","year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.531792Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:5feea5d0bef70b6678bb6d91793014916b7bb8bb6239603e6b41b626d196ba80","observation_id":"735ebe7e-d8f7-49eb-ab40-3920241355da","resolution":{"observed_at":"2026-08-07T11:58:49.580907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:49.179481Z","title":null,"venue":null,"work_id":"2574e889-6e47-4fb5-96dd-997ff79f7803","year":2019},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.673298Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:6aa2bef0d1b8c513a935f400c1c9df2649c90e5bfdab60a78f09e74db8d7cb37","observation_id":"1d6d021a-ebe2-4a93-93d4-edaf5439d032","resolution":{"observed_at":"2026-08-07T11:58:49.303163Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:47.518160Z","title":null,"venue":null,"work_id":"621f1b66-14fe-4702-a29e-f2644e93eda1","year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.885371Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:b8f2a790f0d7cb9876766f57b3050a7a42fa1ac8e64e69623e25601868ad0165","observation_id":"3f4eac09-abdf-401e-a20a-e8d47acada08","resolution":{"observed_at":"2026-08-07T11:58:47.623063Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:47.262772Z","title":null,"venue":null,"work_id":"4f77436d-4e03-4394-aee2-9d911e592ce7","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.012007Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:f6b9b6fc1acd05b26a6911bca05b0c6b43e27bdd79b178c8621bb286f5fc2481","observation_id":"62fa257e-1890-4ca2-8110-3bb13e51b247","resolution":{"observed_at":"2026-08-07T11:58:47.380464Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:47.011650Z","title":null,"venue":null,"work_id":"f0292f7f-95f0-4a07-b918-0935e1cb5d4f","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.135778Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:1f978635ab0a5ef7068bbf412393a64486202d93762eb9001c6841c19f90ffe0","observation_id":"7dfb181e-0192-4418-a9cf-d47f2df7d8c5","resolution":{"observed_at":"2026-08-07T11:58:47.133274Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:46.794738Z","title":null,"venue":null,"work_id":"a3709f3c-62e0-415a-ac65-b50af8fb75f4","year":2025},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.312471Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9796363fb1f65de46eef2b068041a8bc693e9b9fbd91feeffbf3189c779995fe","observation_id":"3a9b166f-7de4-4491-b01c-266595cafe5d","resolution":{"observed_at":"2026-08-07T11:58:46.908162Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:46.555119Z","title":null,"venue":null,"work_id":"be1bd9e3-a965-47fd-925a-417dace50a9e","year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.450645Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:07395237b59da4e37f5d6ba2b31aa05dcadcf7e262ef440cc4bf97c26d83df46","observation_id":"4dd04e83-369d-4ac1-a294-265ae9108f54","resolution":{"observed_at":"2026-08-07T11:58:46.670826Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:48.212075Z","title":null,"venue":null,"work_id":"a3427997-1cbb-4952-a0c5-e180b09554f2","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.481880Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:13f685cbd9ce27c3803eda73f585208dc268bf75c0ee0ae3138383423a717a2b","observation_id":"cf810de3-22e1-4e85-a35f-05c20988db05","resolution":{"observed_at":"2026-08-07T11:58:48.319339Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:47.989759Z","title":"InThe Thirteenth International Conference on Learning Representations","venue":null,"work_id":"92285934-559c-4bb4-8ea0-fe460c553cd9","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.585187Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:3d9b43e25ee12de0896e14ffdef1d86a397d11b19c6dd5f6c08dd95ee46ac887","observation_id":"252f6265-ec33-4877-8adb-686eab9ca844","resolution":{"observed_at":"2026-08-07T11:58:48.107445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:47.753336Z","title":null,"venue":null,"work_id":"a0c59ca5-d6ef-4014-b213-1029209a8405","year":2021},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.752720Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:91ca6bec662b8ab73063b2227d03485fdde061b2774f6743fceda1b9a2159fe9","observation_id":"3dc0ff0c-b6f9-4ff6-ba07-b55adcf33268","resolution":{"observed_at":"2026-08-07T11:58:47.858744Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:45.934079Z","title":null,"venue":null,"work_id":"5af3bc69-ccd4-4ddb-9624-68e04f38f645","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:44.063888Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:7a538dbc62b57d3d561245f3428d962b833dc953bd6268331878958f80efe525","observation_id":"b7613225-cf2f-494c-81b9-276d0398bca5","resolution":{"observed_at":"2026-08-07T11:58:46.049753Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:45.643313Z","title":null,"venue":null,"work_id":"041a8a45-0b12-415f-bf40-a54eebdbdb5d","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:44.194506Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:4dc6db8510d6dc44a1846ff05fde2a82551ebe80caba902f58f6d806999dd3af","observation_id":"816bd5e6-5865-477f-8edf-cf4d553ba731","resolution":{"observed_at":"2026-08-07T11:58:45.782921Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:45.415671Z","title":null,"venue":null,"work_id":"9d8a2b82-39fa-4e54-ad9d-18dbe12e323c","year":2022},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:44.400023Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:20d2ce7d580cdc69f1a6c8323e8ad161fb5542a1a6a2431b9b365ddae1aa4e23","observation_id":"91c72977-bdd7-4b22-89ed-6b0f31f491e0","resolution":{"observed_at":"2026-08-07T11:58:45.501023Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:43.579499Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.579499Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:539621d5b6940b1eeb7ad12320f2dd908d167b04215713c9f6c14d318287b792","observation_id":"e78ae008-1c9e-4c39-978e-9baf14f91428","resolution":{"observed_at":"2026-08-07T11:58:43.579499Z","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":"2026-08-07T11:58:43.751927Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.751927Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:9f90eeb3cd6238c2d3ca70d3c177797240c9bcfda177ffe3487cea383d827ae6","observation_id":"9fc44396-842f-4479-8446-1b9fd3bf05d7","resolution":{"observed_at":"2026-08-07T11:58:43.751927Z","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":"2026-08-07T11:58:46.252786Z","title":null,"venue":null,"work_id":"a8e83cbe-7bb1-4e6d-91d6-251d8392175a","year":2023},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:43.896262Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:cc635db2e0dcd69ceacb36d388f455b7f1c9610f5779eb723b3ca27e34f1b13d","observation_id":"adfc1fac-8d4e-46eb-a438-6e2bd9eff079","resolution":{"observed_at":"2026-08-07T11:58:46.413035Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:32.030448Z","title":"InInternational Conference on Machine Learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:32.030448Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:7de282ab7edd4b1e7cab0aed31feb970aac540f1d2bf8175d511f5d3a94eeb68","observation_id":"d300c0d9-6724-4361-94c7-c10b4982008e","resolution":{"observed_at":"2026-08-07T11:58:32.030448Z","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":"2026-08-07T11:58:48.438479Z","title":"InProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","venue":null,"work_id":"cee8c0a1-67c7-43c5-bf35-a77ace27303b","year":1969},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:42.346666Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:72d5dad6b79f9beff74ac77f696c83cf7c626666792dac810c33d9eeb22a7241","observation_id":"32f6ce44-be7f-47f4-a071-79b4dc16cb12","resolution":{"observed_at":"2026-08-07T11:58:48.553641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:56.982723Z","title":"InProceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol","venue":null,"work_id":"98dce963-2ad7-445a-aa7e-9369b7d14f7b","year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:36.750633Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:5ecc5b32bc4b0c8de4a73dcfcd749a2504fba38b0adceadbba2833819925af9e","observation_id":"78d2be17-9dd3-4389-b919-098810fdb8d7","resolution":{"observed_at":"2026-08-07T11:58:57.118886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T11:58:50.465581Z","title":"Advances in Neural Information Processing Systems37 (2024), 128516–128555","venue":null,"work_id":"f291c828-131e-479a-a7e0-e16b8cc359c5","year":2024},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:41.014751Z"},"links":{"citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:0f19e3483c4f767438c26bc86e8d3e2865c83843260a174d16324ac8f0713485","observation_id":"5e5fe40d-d2fb-4ded-b1cb-e729600bb627","resolution":{"observed_at":"2026-08-07T11:58:50.550251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23565","last_updated":"2025-05-29T15:39:12Z","snapshot_observed_at":"2026-08-08T01:30:53.254755Z","submitted_at":"2025-05-29T15:39:12Z","title":"DRO: A Python Library for Distributionally Robust Optimization in Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23565","snapshot_observed_at":"2026-08-07T11:58:38.461206Z","title":"arXiv:2505.23565 [cs.LG] https://arxiv.org/abs/2505.23565","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T11:58:38.461206Z"},"links":{"cited_paper":"/paper/2505.23565","citing_paper":"/paper/2506.00969"},"observation_digest":"sha256:13eb5a05ce1a7676830a0107a54ddff271e23d3bf0f8e22c70996d2a4089a7dd","observation_id":"10181415-8ba9-4ab8-83e6-6c0bfb2c5783","resolution":{"observed_at":"2026-08-07T11:58:38.461206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.00969","last_updated":"2025-06-01T11:36:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T15:51:55.229890Z","submitted_at":"2025-06-01T11:36:56Z","title":"Data Heterogeneity Modeling for Trustworthy Machine Learning"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":91,"verified_exact":2,"verified_fuzzy":7},"total_outbound_references":100},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 0 inbound Pith citation observations for arXiv:2506.00969."}