{"as_of":"2026-08-10T15:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:13ef719a3fed8c714f9eb98b0cd06d0e51a69594009f76e0ffdab6bae73d15f7","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T00:51:05.033656Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T09:10:59.346861Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"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":"2026-08-10T00:51:05.033656Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18084","last_updated":"2025-01-30T01:42:51Z","snapshot_observed_at":"2026-08-10T10:49:25.282681Z","submitted_at":"2025-01-30T01:42:51Z","title":"U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T00:51:05.033656Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2501.18084"},"observation_digest":"sha256:19aa4915a68f1d73ee76484d3daa4c1f4c60870c153132adf99f5d69811db9db","observation_id":"7a03e858-6014-4227-ab8e-82e65b51c2b1","resolution":{"observed_at":"2026-08-10T00:51:05.033656Z","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":"2026-08-07T15:08:13.082707Z","title":"Diagnosing model perfor- mance under distribution shift","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16226","last_updated":"2025-05-22T04:55:01Z","snapshot_observed_at":"2026-08-09T11:12:11.940622Z","submitted_at":"2025-05-22T04:55:01Z","title":"Realistic Evaluation of TabPFN v2 in Open Environments","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:08:13.082707Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2505.16226"},"observation_digest":"sha256:f04eea5d6bccf0b495f3eaea27c245cf6efdfbacf501ba549a3780ec9fc9caf8","observation_id":"57071a91-97b6-47d2-b2eb-68a92ec7c0af","resolution":{"observed_at":"2026-08-07T15:08:13.082707Z","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":"2026-08-07T13:56:35.235536Z","title":"Diagnosing model performance under distribution shift","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20634","last_updated":"2026-05-28T04:48:09Z","snapshot_observed_at":"2026-08-07T13:47:48.205524Z","submitted_at":"2025-05-27T02:20:50Z","title":"Explaining Concept Shift with Interpretable Feature Attribution","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T13:56:35.235536Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2505.20634"},"observation_digest":"sha256:6c0d2bef42ce9364cbcc0f75373a73ef0d2457aed8ddfd923240fd50dcdf1cd3","observation_id":"07b64a80-f40e-43f7-a471-c11944316d2a","resolution":{"observed_at":"2026-08-07T13:56:35.235536Z","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":"2026-08-07T12:04:30.799496Z","title":"T., Namkoong, H., and Yadlowsky, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00756","last_updated":"2025-05-31T23:50:54Z","snapshot_observed_at":"2026-08-10T07:33:05.969874Z","submitted_at":"2025-05-31T23:50:54Z","title":"\"Who experiences large model decay and why?\" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:04:30.799496Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2506.00756"},"observation_digest":"sha256:c2894a249177c32c15267d420d8746fac8676870b41e97f8792eb150a71b706d","observation_id":"b029dc02-db58-4033-b194-8af9a0245f62","resolution":{"observed_at":"2026-08-07T12:04:30.799496Z","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":"2026-08-09T11:08:53.269513Z","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:ef2461b259968f55ae93d25b7394a6391b8e659018476243c29b86ab7a2543d0","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":"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":"2026-08-07T11:56:22.656281Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.01191","last_updated":"2025-06-01T21:58:09Z","snapshot_observed_at":"2026-08-09T04:49:13.653824Z","submitted_at":"2025-06-01T21:58:09Z","title":"Uncovering Bias Mechanisms in Observational Studies","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:56:22.656281Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2506.01191"},"observation_digest":"sha256:e2bdcd50eee0d8310759fb7154faf24241a851f054097c1f543a510096f7d188","observation_id":"383c99aa-ce44-430d-9452-ff8568a112b1","resolution":{"observed_at":"2026-08-07T11:56:22.656281Z","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":"2026-08-07T00:47:16.777113Z","title":"T., Namkoong, H., and Yadlowsky, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12829","last_updated":"2025-08-22T17:06:00Z","snapshot_observed_at":"2026-08-08T21:07:26.809715Z","submitted_at":"2025-06-15T12:18:05Z","title":"General and Estimable Learning Bound Unifying Covariate and Concept Shifts","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T00:47:16.777113Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2506.12829"},"observation_digest":"sha256:7614a8f40abddb53e6fa7f3ad109db227aba8a81999e7970c4e6bda48c05c113","observation_id":"0cb00691-98cb-4c59-822d-ffe8536943a9","resolution":{"observed_at":"2026-08-07T00:47:16.777113Z","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":"2026-08-06T23:28:49.824756Z","title":"T., Namkoong, H., and Yadlowsky, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.17989","last_updated":"2025-06-22T11:01:41Z","snapshot_observed_at":"2026-08-10T13:32:45.568901Z","submitted_at":"2025-06-22T11:01:41Z","title":"Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T23:28:49.824756Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2506.17989"},"observation_digest":"sha256:b61e6a7e1f6794c5bd5b781dc041c685f827a44c0eb2c05b704675daf00dead1","observation_id":"3bc0ac7b-7f47-4472-9094-4e0ec7625b97","resolution":{"observed_at":"2026-08-06T23:28:49.824756Z","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":"2026-08-05T13:00:04.021229Z","title":"T.; Namkoong, H.; and Yadlowsky, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01060","last_updated":"2025-09-04T17:23:54Z","snapshot_observed_at":"2026-08-10T13:07:01.858332Z","submitted_at":"2025-09-01T02:05:39Z","title":"When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-05T13:00:04.021229Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2509.01060"},"observation_digest":"sha256:de43361a1253060fd9fc85dc443f1fbb2501feb9d361e81174eb5a00c308f885","observation_id":"c58ab6a3-cbfb-4ff2-a9ed-f464cf7b20a0","resolution":{"observed_at":"2026-08-05T13:00:04.021229Z","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":"2303.02011","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2303.02011","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Diagnosing model perfor- mance under distribution shift","venue":null,"work_id":"6190ec8d-3878-4b89-8b78-cefeec436b89","year":2023},"citing_paper":{"arxiv_id":"2604.11200","last_updated":"2026-04-13T08:56:51Z","snapshot_observed_at":"2026-08-02T15:34:18.635189Z","submitted_at":"2026-04-13T08:56:51Z","title":"ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T16:13:22.301367Z"},"links":{"cited_paper":"/paper/2303.02011","citing_paper":"/paper/2604.11200"},"observation_digest":"sha256:7fcee917483a0896d0b76721dca761e1b9304e41f0d2f99bd14451c63b7a75ee","observation_id":"cdc66e20-7b2c-4155-9806-339f41c897bf","resolution":{"observed_at":"2026-05-11T09:10:59.350770Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2303.02011/citation-record","integrity":"/paper/2303.02011/integrity","json":"/paper/2303.02011/citation-record.json","paper":"/paper/2303.02011"},"outbound":[],"paper":{"arxiv_id":"2303.02011","last_updated":"2023-07-10T20:46:14Z","latest_version":4,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-07T19:54:44.992898Z","submitted_at":"2023-03-03T15:27:16Z","title":"Diagnosing Model Performance Under Distribution Shift"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2303.02011."}