{"as_of":"2026-08-15T19:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2309876083b747c8c293b0f1f20b2157a1bbea834a026cda77ddea4e88fef273","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:36:01.441208Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:35:59.223939Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T21:36:01.598553Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2506.23824","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.23824","snapshot_observed_at":"2026-08-06T21:36:01.598553Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","venue":"cs.LG","work_id":"96fb905c-dd02-47a6-b70c-110a44ccf56f","year":2025},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.223939Z"},"links":{"cited_paper":"/paper/2506.23824","citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:9e72da6cccd1a68f0121098a1fce1e9d4479e17d5decd30700d50a8be5fdafa2","observation_id":"73435cfc-5001-4d66-b6d0-fffa1324b8b8","resolution":{"observed_at":"2026-08-06T21:36:01.658167Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.23824/citation-record","integrity":"/paper/2506.23824/integrity","json":"/paper/2506.23824/citation-record.json","paper":"/paper/2506.23824"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2506.23824","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.23824","snapshot_observed_at":"2026-08-06T21:36:01.598553Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","venue":"cs.LG","work_id":"96fb905c-dd02-47a6-b70c-110a44ccf56f","year":2025},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.223939Z"},"links":{"cited_paper":"/paper/2506.23824","citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:9e72da6cccd1a68f0121098a1fce1e9d4479e17d5decd30700d50a8be5fdafa2","observation_id":"73435cfc-5001-4d66-b6d0-fffa1324b8b8","resolution":{"observed_at":"2026-08-06T21:36:01.658167Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:06.024201Z","title":"For a more extensive survey the interested reader is referred to [3, 12]","venue":null,"work_id":"728c4abd-9ece-4338-9cff-76804e0efb75","year":null},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.257944Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:334076706274460c58dfa47491c9202d7d0a7239f3d58782782d3127fa245920","observation_id":"68429964-3b88-4a8c-9e25-ecf6b8eb2d44","resolution":{"observed_at":"2026-08-06T21:36:06.103877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:05.838534Z","title":"Clustering Module As the key building block of our SSL approach, we first de- scribe the CM introduced in [13]","venue":null,"work_id":"20f4f7b6-fb45-4bc9-b505-fb9aef3a6aac","year":null},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.332861Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:cd9394f50d46a9edd30c307db5cc3a27aeb2e7ac7cb90485cc4897410f30701d","observation_id":"60d9e912-dacf-47de-8f4d-01d9dcafc732","resolution":{"observed_at":"2026-08-06T21:36:05.943966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:05.700605Z","title":"We follow the recommendations of [17] for data pre- possessing, model architecture, and training protocol","venue":null,"work_id":"a3a1bdb2-703c-4d66-8a06-c1102aa4df2e","year":null},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.401791Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:a6e9a8542394720c3c9b3bed1ff759364ef1dcbc4514e1be60ef18fb191ab004","observation_id":"505a34eb-dc7a-4f69-b50a-461abd7931b0","resolution":{"observed_at":"2026-08-06T21:36:05.769134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:05.303283Z","title":null,"venue":null,"work_id":"037a53b7-212f-46a9-a4a5-6d53ed2fde9f","year":2000},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.582825Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:055a07cd73bc245315e159de7a20ce38a0a7e857cc9dd4bc95611a01cb2a801f","observation_id":"09b5d286-fff0-4a24-a64d-6d9b40cbb351","resolution":{"observed_at":"2026-08-06T21:36:05.400290Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:05.095053Z","title":"Our training strategy benefits from the built-in cluster- ing capability of the CM module and does not rely on com- plex training schemes","venue":null,"work_id":"f9364f30-4f4e-46ae-b69b-9d7c7ca0568a","year":null},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.673944Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:ac932f868703efb28ecde2e737b2f7646f55e8dc2b3d8473277a7803692eadfb","observation_id":"a28800bd-e138-4060-a832-aa435a386706","resolution":{"observed_at":"2026-08-06T21:36:05.142028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:04.007791Z","title":"There are many consistent expla- nations of unlabeled data: Why you should average,","venue":null,"work_id":"0fe51c89-1e3b-4453-954f-5cb76d17afbd","year":2019},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.213631Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:4f65e416d784705d74f9617031686ea714f10d6ec2431b152e0540b7c8c5ebbd","observation_id":"922a8856-b489-46ad-99d2-730d6e7913d4","resolution":{"observed_at":"2026-08-06T21:36:04.076889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:04.908964Z","title":"Preparing medical imaging data for machine learning,","venue":null,"work_id":"0381cf43-63a1-457e-9b9d-23996ae03fac","year":2020},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.759647Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:9be7506e15b05f295d015bfa052725c3471d8957c8ea2780dd115cedd4f8772a","observation_id":"8fa6754c-bf19-4b67-b8a3-38b78ac62b2f","resolution":{"observed_at":"2026-08-06T21:36:04.980787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:04.677354Z","title":null,"venue":null,"work_id":"d49fab2b-2cc0-4aa4-9818-cce36ee44796","year":2006},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.798217Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:a9bf419d71728a4a593262b2b0d1fcfc9d36c8fc07fa5a648da45b156f0d9882","observation_id":"b9af9dfc-7d4b-423f-b8bd-f2efb0ca0def","resolution":{"observed_at":"2026-08-06T21:36:04.806031Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00550","last_updated":"2021-08-23T02:54:59Z","snapshot_observed_at":"2026-08-14T01:45:23.826078Z","submitted_at":"2021-02-28T16:22:58Z","title":"A Survey on Deep Semi-supervised Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00550","snapshot_observed_at":"2026-08-06T21:35:59.882841Z","title":"A survey on deep semi-supervised learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.882841Z"},"links":{"cited_paper":"/paper/2103.00550","citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:01b36f920d9044ffa8bfafce906b377e16550a4cd1ba4350b4fe8fdbdb27b0dc","observation_id":"db49320c-6549-481e-8f94-4e2ada2b8824","resolution":{"observed_at":"2026-08-06T21:35:59.882841Z","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":"2026-08-06T21:36:04.511330Z","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,","venue":null,"work_id":"ad3f872e-72dd-4bc6-a160-1628687c2c51","year":2017},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.001901Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:c09fa4cfb9eb5d2df1de20f1782694f1535c610c6a7c18256ccc99d4a05cc1f2","observation_id":"ff01464d-80c5-412c-a46d-12dfd3207093","resolution":{"observed_at":"2026-08-06T21:36:04.583498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:04.369022Z","title":"Temporal ensembling for semi-supervised learning,","venue":null,"work_id":"47cf2607-bbfc-4ecc-8e3b-d029e201a777","year":2017},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.048684Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:8ba540ce2f54cc3bc9b3f249f7089c2d382c91d3d7138a9b8d23fc445dac6a59","observation_id":"a88e8672-930e-4b7f-8e2b-54afdceb3912","resolution":{"observed_at":"2026-08-06T21:36:04.461719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:04.218398Z","title":"Virtual adversarial training: A reg- ularization method for supervised and semi-supervised learning,","venue":null,"work_id":"4bab3d80-54c9-4b46-ab18-4674792b36c5","year":1979},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.137439Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:91fa069ce6d5f8bf0d64cf1b970eb7d04c9dd571dd9843e1aa1eb954a3649900","observation_id":"9da1ecc6-7e55-438f-89ce-dc386edde775","resolution":{"observed_at":"2026-08-06T21:36:04.273413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:02.719833Z","title":"Unsu- pervised deep embedding for clustering analysis,","venue":null,"work_id":"32bf6ca7-f666-4ee3-836f-0d08d9aab4ce","year":2016},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.785597Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:9a232c0e9ba0afb5cf6a63fae2d8c03c11be9848c697ea657a6733b06bb8f990","observation_id":"7410d45a-1970-4b88-931d-f59e0f63ecbd","resolution":{"observed_at":"2026-08-06T21:36:02.843007Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:03.822930Z","title":"Semi-supervised learning by entropy minimization,","venue":null,"work_id":"b07f3c40-9175-4db1-9fa7-7561f52bc239","year":2005},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.278902Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:01b3cffa9f7ef5101dc266057e0994937cba2780aaa23d493a1b202fac93e3e0","observation_id":"36e52401-d540-45e0-9590-faea2e25d9ec","resolution":{"observed_at":"2026-08-06T21:36:03.904358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:03.663289Z","title":"Pseudo-label : The simple and effi- cient semi-supervised learning method for deep neural networks,","venue":null,"work_id":"2a3e1aab-8902-4919-8a78-07df8b07beb0","year":2013},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.367328Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:671df7e1cafc52389f55b81fbb8f779ee2f1caf8725f6f578dcc2c3df02c4b95","observation_id":"094be1a8-2525-4958-8bcf-bff9e1f262bd","resolution":{"observed_at":"2026-08-06T21:36:03.745796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:03.488282Z","title":"Meta pseudo labels,","venue":null,"work_id":"01e79615-69e5-4eaf-9114-b2ec9dea4f96","year":2021},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.449911Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:c794b67c62cacfef942253f6a604f3f84d8440abc0045dae6a5896073d9e076b","observation_id":"9325e847-a13e-4bd2-9ddd-1e52384b769e","resolution":{"observed_at":"2026-08-06T21:36:03.587317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:03.259009Z","title":"S4l: Self-supervised semi-supervised learning,","venue":null,"work_id":"31cafdd1-7442-41d6-addf-f50134c5e6ce","year":2019},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.559927Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:ba03d5ce5d1be6052834bc00c8483f871bf2bd7196577a01214eed2a646a9275","observation_id":"bd454507-fc7a-4a80-a9e8-883597f24565","resolution":{"observed_at":"2026-08-06T21:36:03.371817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:03.092022Z","title":"A Comprehensive Survey on Deep Clus- tering: Taxonomy, Challenges, and Future Directions,","venue":null,"work_id":"51f0b104-1c60-4bed-9693-d919ab5081d9","year":2022},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.645684Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:88673b2e07bf61d4ea01e39fe05ac9fcee104e82d988db8d01a89a1301fdc788","observation_id":"b64d956f-a81b-4dc4-864c-4a47212f920d","resolution":{"observed_at":"2026-08-06T21:36:03.164849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:02.920866Z","title":"Joint optimization of an autoencoder for clustering and embedding,","venue":null,"work_id":"93415c1d-418b-41a8-9e3e-96d7201c2768","year":1901},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.708139Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:5671828d56755e34e0e754b5243dd3c20306d11d1703c61422ec4967ea318a2c","observation_id":"f0e527b7-c329-4e8d-b2dc-3dc13376936b","resolution":{"observed_at":"2026-08-06T21:36:03.024985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:01.713182Z","title":"Aver- aging weights leads to wider optima and better general- ization,","venue":null,"work_id":"a50c9565-89e3-42ea-a816-060730c7cfc9","year":2018},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:01.356998Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:2b38a86cc60e25c1d9c265cc6e9d839f347f5f40e3deef6042ab2b5b23586b77","observation_id":"06a2c5c5-413d-441b-b4e2-62bb814b2045","resolution":{"observed_at":"2026-08-06T21:36:01.811997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:02.576428Z","title":"Deep clustering for unsupervised learning of visual features,","venue":null,"work_id":"d3a1236c-62ba-490f-88fa-1ae248390af4","year":2018},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.878900Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:9821f8409ab8752456c1ac0c3059d88031e0181c1e0cdf8980632a1bc3a8fa57","observation_id":"2f346247-e0d5-42b5-8d5f-46f8cb780de2","resolution":{"observed_at":"2026-08-06T21:36:02.649608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:02.399894Z","title":"Prototypical contrastive learning of unsupervised rep- resentations,","venue":null,"work_id":"5de0cf26-3fe2-4289-bd24-e36ed00a4c50","year":2021},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:00.975115Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:992cacf8991c9013ef51700d3b91b7f94e0bc286e90ac56325abd5dc36c25b85","observation_id":"8d52171f-c2c6-426e-be37-956f228df376","resolution":{"observed_at":"2026-08-06T21:36:02.498309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:02.249903Z","title":"Realistic evaluation of deep semi-supervised learning algorithms,","venue":null,"work_id":"4ac8c3b8-3eee-489c-829e-835f9dafc876","year":2018},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:01.059610Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:133cf2453d08245a4f26dc8a1343c5e64a807c5fc16c6ec415cdc5c73ad999c6","observation_id":"e144a072-5455-4fd7-8174-e0a60555aac9","resolution":{"observed_at":"2026-08-06T21:36:02.329910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:01.146021Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:01.146021Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:dd638cf22eddb06151aebfc05e839f27cab750ad0306f6bcda80c40787aa9a21","observation_id":"f4c434fe-a2d0-4bd6-ba02-9b788bf66a67","resolution":{"observed_at":"2026-08-06T21:36:01.146021Z","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":"2026-08-06T21:36:02.023968Z","title":"Wide resid- ual networks,","venue":null,"work_id":"a4baaaf3-7570-4cba-8bc6-07e6014ef12f","year":2016},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:01.223133Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:5309a48e4084148523683a75ec3191219572abe17939391115234372d0d0ad5b","observation_id":"842c15b7-22b6-4485-8a3c-f7cb5a644a1c","resolution":{"observed_at":"2026-08-06T21:36:02.104962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T21:36:01.883836Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":"e667eb0c-0893-41b5-a7d4-7f69e38ea671","year":2015},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:01.290892Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:f03bbabac173d01939de2e1f5915b0a01d0a92d62ecff85b75e713890c82f40d","observation_id":"42cbd1c8-d76f-47fe-9247-cc1935389337","resolution":{"observed_at":"2026-08-06T21:36:01.959065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03426","last_updated":"2020-09-18T01:56:41Z","snapshot_observed_at":"2026-08-02T15:32:07.466568Z","submitted_at":"2018-02-09T19:39:33Z","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03426","snapshot_observed_at":"2026-08-06T21:36:01.441208Z","title":"UMAP: uniform manifold approximation and projection for dimension reduction,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:01.441208Z"},"links":{"cited_paper":"/paper/1802.03426","citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:59fc76373a2be5a2fc3d0c1243420e55f5b0718950e341bd01eeb1d04d72c06e","observation_id":"de3b90c7-e0e1-4c83-a8d0-a8587825aac6","resolution":{"observed_at":"2026-08-06T21:36:01.441208Z","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":"2026-08-06T21:36:05.516056Z","title":"The hyper-parameters β and δ are tuned over the validation dataset","venue":null,"work_id":"855b67dd-6dca-41d8-bb21-6c764b0c02e3","year":null},"citing_paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-06T21:35:59.457163Z"},"links":{"citing_paper":"/paper/2506.23824"},"observation_digest":"sha256:45bd6a096eb0296e43bda579e6d4c8efa913570d1abf34d7ff27bbc9747c17f5","observation_id":"86575c3a-b29b-4d1f-a27d-6dc390adaf82","resolution":{"observed_at":"2026-08-06T21:36:05.611642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.23824","last_updated":"2025-06-30T13:17:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T21:28:22.291816Z","submitted_at":"2025-06-30T13:17:08Z","title":"Supercm: Revisiting Clustering for Semi-Supervised Learning"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":23},"total_outbound_references":29},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2506.23824."}