{"as_of":"2026-08-09T19:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5bd455ab6fa7d7967dfc37ec2be18be62318c677f328ef073b55e899212b971d","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:28:35.844364Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":2271,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2002.05709","last_updated":"2020-07-01T00:09:08Z","snapshot_observed_at":"2026-08-02T19:54:26.138356Z","submitted_at":"2020-02-13T18:50:45Z","title":"A Simple Framework for Contrastive Learning of Visual Representations","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-13T18:31:53.283456Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2002.05709"},"observation_digest":"sha256:f3142f372eaee58f544a8994bb041dfc7c831e4787e63f5c04006bef73ba7f4e","observation_id":"a55011df-ad1a-43c1-a3c0-10d21d8374a7","resolution":{"observed_at":"2026-05-13T18:31:53.330702Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2404.08471","last_updated":"2024-02-15T18:59:11Z","snapshot_observed_at":"2026-08-07T02:30:11.447693Z","submitted_at":"2024-02-15T18:59:11Z","title":"Revisiting Feature Prediction for Learning Visual Representations from Video","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-12T12:40:23.709098Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2404.08471"},"observation_digest":"sha256:90447b8d0f801a2653fbbe9ad5ea13a77ba36aab49115d4714eefe71f5c5dda4","observation_id":"6bfe0500-08b2-4d8e-9faa-9a3a3521a86f","resolution":{"observed_at":"2026-05-12T12:40:24.067510Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-07T05:28:35.844364Z","title":"D., Kurakin, A., Zhang, H., and Raffel, C","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08070","last_updated":"2025-06-20T02:52:55Z","snapshot_observed_at":"2026-08-09T16:39:21.865685Z","submitted_at":"2025-06-09T17:04:11Z","title":"Info-Coevolution: An Efficient Framework for Data Model Coevolution","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T05:28:35.844364Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2506.08070"},"observation_digest":"sha256:6f0b3476e3b55df03c2bee0671db0d8e8b9c04659dd1bde151f197edd84c8bd1","observation_id":"dc0d371b-e316-4021-9887-d56a50fe028f","resolution":{"observed_at":"2026-08-07T05:28:35.844364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-06T01:05:33.259087Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.03955","last_updated":"2025-08-05T22:44:36Z","snapshot_observed_at":"2026-08-06T21:16:50.885444Z","submitted_at":"2025-08-05T22:44:36Z","title":"Scaling Up Audio-Synchronized Visual Animation: An Efficient Training Paradigm","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T01:05:33.259087Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2508.03955"},"observation_digest":"sha256:a464d1fbac1b1599787b4f0ea41097d0258bcc0ade3f6db4eaa0d193a3405a90","observation_id":"b1636644-22d8-4e04-89b3-013072baaced","resolution":{"observed_at":"2026-08-06T01:05:33.259087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2605.08519","last_updated":"2026-05-08T22:03:31Z","snapshot_observed_at":"2026-08-03T01:40:58.624663Z","submitted_at":"2026-05-08T22:03:31Z","title":"SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data","version":1},"reference_index":112,"source":"arxiv_source","source_observed_at":"2026-05-12T01:31:00.029032Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2605.08519"},"observation_digest":"sha256:aec2cc58afab3170bf67b2a421d9cd202b01db966129c4898404e52556003f00","observation_id":"5ea45077-4453-4572-b446-0f81a0a10120","resolution":{"observed_at":"2026-05-12T07:56:27.565187Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2605.12387","last_updated":"2026-05-12T16:50:54Z","snapshot_observed_at":"2026-08-06T08:42:38.154690Z","submitted_at":"2026-05-12T16:50:54Z","title":"A Semi-Supervised Framework for Speech Confidence Detection using Whisper","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-13T04:00:49.889795Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2605.12387"},"observation_digest":"sha256:cabaa8dace74d76da53f7be3d2549a9c4f3d6a9c9832006eb4b96492ed78b51e","observation_id":"348912c1-6355-4f5e-b073-ab87cb11b73e","resolution":{"observed_at":"2026-05-13T04:02:13.218719Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2605.17433","last_updated":"2026-06-20T05:19:32Z","snapshot_observed_at":"2026-08-08T21:34:49.848688Z","submitted_at":"2026-05-17T12:57:29Z","title":"VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-20T13:10:42.631408Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2605.17433"},"observation_digest":"sha256:7e39b75f8eeef3e5d8211cce44f3b93b8b1c41bd9c5738c4e6ec30b7f393d68f","observation_id":"e97a47ce-fcfe-4866-8a21-195f9cfe3763","resolution":{"observed_at":"2026-05-20T13:13:18.446004Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2605.17433","last_updated":"2026-06-20T05:19:32Z","snapshot_observed_at":"2026-08-08T21:34:49.848688Z","submitted_at":"2026-05-17T12:57:29Z","title":"VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T19:15:46.593842Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2605.17433"},"observation_digest":"sha256:648a0074054b3202fae8f8159ad7cbf0c5119c9a79647a0a9cdd7a00f918f590","observation_id":"19026fe0-17db-46f9-a551-fd53d04e25f8","resolution":{"observed_at":"2026-07-01T14:55:48.031018Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2606.05347","last_updated":"2026-06-23T12:14:36Z","snapshot_observed_at":"2026-08-08T13:42:10.074427Z","submitted_at":"2026-06-03T18:47:54Z","title":"TopoPult-SSL: Gland-Mask-Free Cross-Device Meibomian Gland Segmentation via Self-Distilled Weak Clinical Priors","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T06:12:45.531363Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2606.05347"},"observation_digest":"sha256:960bdb31ab6e5c7f8e5aee84cf9b9594f1a1b25f9d0faff9e40b8fa7eadf81e8","observation_id":"153252fe-0422-4d4f-8938-84b5fce2e973","resolution":{"observed_at":"2026-06-28T06:21:42.972067Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":"2001.07685","doi":"10.48550/arxiv.2001.07685","metadata_source":"arxiv_reference","pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"In: Advances in Neural Information Processing Systems","venue":"arXiv (Cornell University)","work_id":"48630ac5-42de-4f09-8122-5933ca736859","year":2001},"citing_paper":{"arxiv_id":"2606.11626","last_updated":"2026-06-10T03:41:50Z","snapshot_observed_at":"2026-08-06T04:25:55.162237Z","submitted_at":"2026-06-10T03:41:50Z","title":"Adapting Vision-Language Models from Iconic to Inclusive for Multi-Label Recognition Without Labels","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-06-27T10:28:48.833014Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2606.11626"},"observation_digest":"sha256:f3c19c37e2c336c7c4ceabdd292ddc865d638c6d36f1e0ab9d57a343eca45457","observation_id":"ae54e817-7f46-4d01-b1a7-97c0b9c0e4f9","resolution":{"observed_at":"2026-07-03T09:07:48.266435Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07685","snapshot_observed_at":"2026-08-01T00:54:33.081511Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.26014","last_updated":"2026-07-28T17:27:36Z","snapshot_observed_at":"2026-08-04T17:32:44.956215Z","submitted_at":"2026-07-28T17:27:36Z","title":"Automated Solar Radio Burst Detection Using Deep Learning on Augmented e-Callisto Data","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-01T00:54:33.081511Z"},"links":{"cited_paper":"/paper/2001.07685","citing_paper":"/paper/2607.26014"},"observation_digest":"sha256:8b511cdb8d18f7d8c2708c13df60a2f3c591099200eb501f4786c3a879c9dc6f","observation_id":"d02de5f9-5878-464e-b912-b0952aa9d689","resolution":{"observed_at":"2026-08-01T00:54:33.081511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2001.07685/citation-record","integrity":"/paper/2001.07685/integrity","json":"/paper/2001.07685/citation-record.json","paper":"/paper/2001.07685"},"outbound":[],"paper":{"arxiv_id":"2001.07685","last_updated":"2020-11-25T17:22:06Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T00:24:47.113777Z","submitted_at":"2020-01-21T18:32:27Z","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2001.07685."}