{"as_of":"2026-07-23T05:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:48bfd76dbe2bab13569d70d9572e2f93fc0096df5fef5e9cd8df29442c228ab1","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-07-22T06:31:00.163083+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-02T19:58:33.810532Z","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":"2026-07-10T06:15:00.866473Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2605.08448","last_updated":"2026-05-08T20:15:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:15:40Z","title":"LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-12T00:54:53.402959Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2605.08448"},"observation_digest":"sha256:143620f5c054a08022b8f3166a9c022f8ebc5bf740a79c0c3e1f291783bd492a","observation_id":"d81c4e08-aa60-417d-9ab5-271bd7688bcc","resolution":{"observed_at":"2026-05-12T00:56:14.259248Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2606.04516","last_updated":"2026-06-03T06:47:50Z","snapshot_observed_at":"2026-07-06T23:44:37.797802Z","submitted_at":"2026-06-03T06:47:50Z","title":"GeoMin: Data-Efficient Semi-Supervised RLVR via Geometric Distribution Modeling","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-06-28T07:45:43.320339Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2606.04516"},"observation_digest":"sha256:4cacc88184df684199fbdddc633435631e108e05b339c272bd892922ea04598a","observation_id":"5e9dcf80-3308-4a6e-b199-6fab04fab246","resolution":{"observed_at":"2026-07-02T06:06:40.749783Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2606.21493","last_updated":"2026-06-19T14:42:52Z","snapshot_observed_at":"2026-07-06T23:56:31.454439Z","submitted_at":"2026-06-19T14:42:52Z","title":"Semi-Supervised Vision-Language-Action Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T14:15:59.690149Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2606.21493"},"observation_digest":"sha256:621defb7296da3a83e73d944e37f7f66e18f4804ae02963059c4c9b0f2833560","observation_id":"87337021-85a2-446e-93f2-04ba2f56bc4d","resolution":{"observed_at":"2026-07-04T06:39:38.080741Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2606.24985","last_updated":"2026-06-23T14:24:43Z","snapshot_observed_at":"2026-07-06T23:59:28.120338Z","submitted_at":"2026-06-23T14:24:43Z","title":"Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection","version":1},"reference_index":209,"source":"arxiv_source","source_observed_at":"2026-06-26T00:27:41.609691Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2606.24985"},"observation_digest":"sha256:95d018325acd9def7dfc9eab758dc95be2da26e1869f23a69dc491991d2c2646","observation_id":"cda7beae-c621-4445-bb0a-9d4143fe1ddc","resolution":{"observed_at":"2026-06-26T00:28:43.069417Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2606.26973","last_updated":"2026-06-25T12:45:42Z","snapshot_observed_at":"2026-07-07T00:01:15.803410Z","submitted_at":"2026-06-25T12:45:42Z","title":"Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T05:00:28.839647Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2606.26973"},"observation_digest":"sha256:1fae6fba50121959ac24e50d79e4b8d50078395ff1581e9530c97b234bcebaeb","observation_id":"ce55985d-deed-4144-802c-78a392aa1452","resolution":{"observed_at":"2026-07-04T13:39:51.071464Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2606.30577","last_updated":"2026-06-30T14:59:05Z","snapshot_observed_at":"2026-07-07T00:04:25.385438Z","submitted_at":"2026-06-29T17:20:26Z","title":"APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms","version":1},"reference_index":246,"source":"pdf_text","source_observed_at":"2026-06-30T06:09:54.742202Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2606.30577"},"observation_digest":"sha256:0d884c4235429b701d4020f7e24f48d5152caadfa4cd8e967b8ab387fcb5f669","observation_id":"03347aba-7b20-4bc8-899b-70d6f7557ed5","resolution":{"observed_at":"2026-06-30T06:14:19.489581Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2606.30577","last_updated":"2026-06-30T14:59:05Z","snapshot_observed_at":"2026-07-07T00:04:25.385438Z","submitted_at":"2026-06-29T17:20:26Z","title":"APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms","version":2},"reference_index":225,"source":"pdf_text","source_observed_at":"2026-07-01T06:38:06.306930Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2606.30577"},"observation_digest":"sha256:c72bbb1a75c44b5071d8da587a3298f8085a6346aa7d9bf396455b4e89e17204","observation_id":"681bd27d-ac11-435d-8a3f-8e5c340a304e","resolution":{"observed_at":"2026-07-01T09:05:37.592348Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2607.00113","last_updated":"2026-06-30T19:50:18Z","snapshot_observed_at":"2026-07-07T00:05:49.557137Z","submitted_at":"2026-06-30T19:50:18Z","title":"SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-07-02T19:58:33.810532Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2607.00113"},"observation_digest":"sha256:32b7f58fe0791c6e34567a993a4e28afc0141b9dcdf769bf042c8a33f9e4a890","observation_id":"7f1903e0-293f-400e-81be-3ecf829fcddb","resolution":{"observed_at":"2026-07-02T20:07:21.130438Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","version":3},"cited_work":{"arxiv_id":"2205.07246","doi":"10.48550/arxiv.2205.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07246","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"arXiv preprint arXiv:2205.07246 , year=","venue":null,"work_id":"5b1d3b4b-0a9b-4f96-8c42-62636f1fa602","year":2022},"citing_paper":{"arxiv_id":"2607.00358","last_updated":"2026-07-01T02:57:06Z","snapshot_observed_at":"2026-07-07T00:06:03.968882Z","submitted_at":"2026-07-01T02:57:06Z","title":"PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-02T16:45:46.207051Z"},"links":{"cited_paper":"/paper/2205.07246","citing_paper":"/paper/2607.00358"},"observation_digest":"sha256:3c57f0a30517573a357fac378912a546c14a4c3f07d9225523d73b9a186677e2","observation_id":"f2e4fb03-26ce-4c3b-903f-426572652d6c","resolution":{"observed_at":"2026-07-02T16:47:08.983508Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2205.07246/citation-record","integrity":"/paper/2205.07246/integrity","json":"/paper/2205.07246/citation-record.json","paper":"/paper/2205.07246"},"outbound":[],"paper":{"arxiv_id":"2205.07246","last_updated":"2023-01-31T10:04:52Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T13:10:06.071526Z","submitted_at":"2022-05-15T10:07:52Z","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning"},"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":"2026-07-22T06:31:00.163083+00:00","source":"crossref"},{"observed_at":"2026-07-22T06:30:55.410749+00:00","source":"retraction_watch"}],"thesis":"As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2205.07246."}