{"as_of":"2026-08-07T22:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:749a9da519d18e191296d6ac64c57932dbef15f8373a2acf9ac2c3fb8ff6b3b5","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:23:54.659469Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.00411/citation-record","integrity":"/paper/2507.00411/integrity","json":"/paper/2507.00411/citation-record.json","paper":"/paper/2507.00411"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:24:01.074922Z","title":"Deep learning from noisy image labels with quality embedding,","venue":null,"work_id":"a45684b9-c90b-4fc2-a525-349241669aaa","year":1909},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:48.462814Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:72876ae9bb8c9e9bdb3a4e0dbede6a838cd942ec3d7500e3cbc6dc98d59f1fc8","observation_id":"e3dc68fd-b65a-4db5-a2a6-678a9ed4d7d0","resolution":{"observed_at":"2026-08-06T21:24:01.227237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:24:00.786165Z","title":"Learning from partial labels,","venue":null,"work_id":"e879d958-bcc8-42a8-b04b-103aac357b42","year":2011},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:48.540127Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:f2c68ec971b8c2a5031194030d0fefa5ad6d63fd263f9ab7c1ca13bef89f4598","observation_id":"78d7aef7-37e2-4723-9cf3-7e96bf636f41","resolution":{"observed_at":"2026-08-06T21:24:00.905791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:24:00.429158Z","title":"Partial label learning with discrimination augmentation,","venue":null,"work_id":"881fee04-371e-4c04-807d-e41f25b1acb7","year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:48.700600Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:19dc294d266f36e15fc49311ccef83b0863c33722d664d0230ae803e11d8dc19","observation_id":"63544471-5662-44e0-8f8c-fb7d28b4e376","resolution":{"observed_at":"2026-08-06T21:24:00.608727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:24:00.130213Z","title":"Optimized graph learning using partial tags and multiple features for image and video annotation,","venue":null,"work_id":"76940a54-849e-47a2-a0b6-7fd9ecb27d3c","year":2016},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:48.892211Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:7e936c3d13187321761760a67355000c41d0b56b14ee76ea465c11cd5ca62572","observation_id":"9a913a2d-56ee-41a5-867a-08357eafe1e7","resolution":{"observed_at":"2026-08-06T21:24:00.258754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:59.909222Z","title":"Pseudo label association and prototype- based invariant learning for semi-supervised nir-vis face recognition,","venue":null,"work_id":"98ecd752-40b4-49bf-96b6-ca29a3e9436c","year":2024},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.111786Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:e43b9cfd08f9abd70d1a1dcb391a8f78bec53a8ec4b838daa61dc32bd64b9240","observation_id":"9c000dca-56e8-4f24-9925-5c65f3157c44","resolution":{"observed_at":"2026-08-06T21:23:59.994922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:59.740652Z","title":"Who’s in the picture,","venue":null,"work_id":"8ef5bf39-359a-4606-91ad-2961e3b8c983","year":2004},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.343936Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:3f89711d5b44f5a4da7fe9810b0722cc51dfff8d1684d6f01975d2310654f2a3","observation_id":"dfcb49ea-c6f5-40b1-a71c-3b442df91f52","resolution":{"observed_at":"2026-08-06T21:23:59.797920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:59.585482Z","title":"Learning from candidate labeling sets,","venue":null,"work_id":"b9ee0555-857c-40b7-8a35-a68b59636b69","year":2010},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.434304Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:0c3e34755ef72998ad316f6689b0d5b153bea1d5825d3c7534b28c7304517459","observation_id":"b88a2ac1-23f8-4ba8-845f-cd6040a2fb62","resolution":{"observed_at":"2026-08-06T21:23:59.650938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:59.389598Z","title":"Large margin partial label machine,","venue":null,"work_id":"ab5b72ab-4bb7-4207-800f-593cca3f7d82","year":2019},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.515643Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:83c5ea9fdcde9ba32f477d50b521dccb809645b0e6de15ef5c430f26c166243b","observation_id":"4507adfd-af6f-482e-a7e8-f9e9117cbecd","resolution":{"observed_at":"2026-08-06T21:23:59.463180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:59.218459Z","title":"Deep discriminative cnn with temporal ensembling for ambiguously-labeled image classification,","venue":null,"work_id":"04efb508-c6ca-404c-b6ae-78061ce2fbd9","year":2020},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.613878Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:74ccd5db9236cb35b50ed76a822f565a4f7f8f6a3ac791ccc00855a90767b9e5","observation_id":"818c64d1-5830-4677-b964-d2609f43af86","resolution":{"observed_at":"2026-08-06T21:23:59.316154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:59.013921Z","title":"Network cooperation with pro- gressive disambiguation for partial label learning,","venue":null,"work_id":"660ff661-12ac-46c8-a55d-1bc2b9ea643a","year":2020},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.696993Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:7745319779978eab2c7fa15cd881e874e5d11e32042cc9d59f78e347614948e9","observation_id":"70e4c98a-8839-4c73-be55-a31ef34a41c7","resolution":{"observed_at":"2026-08-06T21:23:59.100605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:58.838100Z","title":"Partial label learning based on disambiguation correction net with graph representation,","venue":null,"work_id":"cff32ca9-f650-42cc-8ebb-58cfae1fa6ea","year":2021},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.819372Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:17178f957489b43e0d0f6e4116f5b72f5ab3208e0f82faad2a8cf44fa69b22ec","observation_id":"14f70178-bb75-4592-b826-b414fe1288d5","resolution":{"observed_at":"2026-08-06T21:23:58.920921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:49.916268Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:49.916268Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:abebafff8894abc111acfe14d84c5f9909d6c421cfa82cb9a9a5fe6f46c81437","observation_id":"f6d80546-ae7c-4334-8e10-8dae43f60999","resolution":{"observed_at":"2026-08-06T21:23:49.916268Z","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":"2026-08-06T21:23:58.631924Z","title":"Nonlinear regularized reaction-diffusion filters for denoising of images with textures,","venue":null,"work_id":"57eb7a0b-33ac-47ae-b2e5-c953e885fc8a","year":2008},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:50.099922Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:cf3594b68670613f8cdb7d15d14a7c5d608fc331fd9abaf91d04ac0bded013cb","observation_id":"42bac88e-3ca7-464d-96c4-5b4ee0b3b864","resolution":{"observed_at":"2026-08-06T21:23:58.701983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:58.471171Z","title":"Card: Classification and regression diffusion models,","venue":null,"work_id":"0ef4180c-2252-4d17-bc5e-8278ddf29508","year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:50.269514Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:11c543b924fb7d8c9e27167603cef6e9543993182a5a4fee03fd9047be5befcf","observation_id":"c7595196-785c-46d3-8b37-6271decf5adf","resolution":{"observed_at":"2026-08-06T21:23:58.530793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:58.328283Z","title":"A conditional multinomial mixture model for superset label learning,","venue":null,"work_id":"5d9fa753-2963-4854-adf0-657cff7dca6f","year":2012},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:50.413023Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:3bc9b0af508c00195e16c8e5b7c5a9cb8f014ce1177626d4eb1d097be224f2c1","observation_id":"3d5b64c4-d10e-4cfb-8804-a4baf6d2aca3","resolution":{"observed_at":"2026-08-06T21:23:58.380631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:58.183724Z","title":"Partial label learning via feature-aware disambiguation,","venue":null,"work_id":"6433c103-7d25-46a9-8995-5e3338fe574a","year":2016},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:50.600285Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:83bd7a9d3d582c23b30f4c23fb973fa455a775970a2cc76d6a0b9ae2121e1c21","observation_id":"0c461b60-1fcc-4047-a35f-6ec15202f2f1","resolution":{"observed_at":"2026-08-06T21:23:58.241972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:58.002736Z","title":"A multi-class partial hinge loss for partial label learning,","venue":null,"work_id":"38fa406b-f5ea-4ac4-a37a-a34d52f2653e","year":2023},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:50.799320Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:80ebef76406892ad0273891c1a455d1c22e0f4b70fcc53561c673e65d53ad131","observation_id":"51233e9e-d63d-4555-8c4a-5faa2af2d83a","resolution":{"observed_at":"2026-08-06T21:23:58.090228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:57.728899Z","title":"Addressing label ambiguity imbalance in candidate labels: Measures and disambiguation algorithm,","venue":null,"work_id":"a70a3682-34ef-4ee9-945d-675293b5ad64","year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:50.965139Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:070afd8612cf13214f41e373017f45a9b33a32ab234e6696f6a9c3fa4cba1078","observation_id":"3f0d3f72-ccd8-4ab3-ac53-a85bdddea58f","resolution":{"observed_at":"2026-08-06T21:23:57.914490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:57.556701Z","title":"Dictionary learning from ambiguously labeled data,","venue":null,"work_id":"ed6aec88-fd19-4154-a962-7b55bcfba473","year":2013},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:51.174646Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:7e056bf3e9a19bc030a7df81fd4d58b84a0ae13f4456f7e6d85106a5981f4643","observation_id":"9ddddbc1-3cfe-4c6f-80e0-04c0a29698b8","resolution":{"observed_at":"2026-08-06T21:23:57.652516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:57.420059Z","title":"Partial label learning with competitive learning graph neural network,","venue":null,"work_id":"fff4767f-cb77-4123-afc2-98cac4a4c6a9","year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:51.350004Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:7bd1c76be83d0254701da1cf5d2d21c23a9626a31cfe6fdac2daf9f33e57ee9b","observation_id":"b096b788-e925-4593-a939-a269536ccc6a","resolution":{"observed_at":"2026-08-06T21:23:57.485831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:57.280990Z","title":"Progressive identification of true labels for partial-label learning,","venue":null,"work_id":"a8c7ecf9-2561-4159-b06c-40c00c2296b7","year":2020},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:51.467517Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:5f5ed788c6dd14bcf04cb8dee18bfb68c7ca32df3cef6f0f6226be7dd7eda5c3","observation_id":"8eee6f97-42d0-4e52-a8c7-937bd5c7c4e9","resolution":{"observed_at":"2026-08-06T21:23:57.342293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:57.141774Z","title":"Leveraged weighted loss for partial label learning,","venue":null,"work_id":"fbd32d16-b195-4512-beac-aa5845ffc06e","year":2021},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:51.620038Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:e8193a1e4f735ce147180e7e68643b088f77f90b8f1053134fbf8347418af653","observation_id":"dcf4d96a-5356-49ae-9a50-9313c1556c7b","resolution":{"observed_at":"2026-08-06T21:23:57.193535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:56.971841Z","title":"Graphdpi: Partial label disambiguation by graph representation learning via mutual information maximization,","venue":null,"work_id":"c16e32bc-b333-4bb1-9354-bd1cad98dd6a","year":2023},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:51.808893Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:911b6dd33fb0e7de4f1f7bc7634cd99dae8effb96ff37070b8edbb4493bde9c0","observation_id":"4bad490f-8dba-43ae-8a29-6281adbf4122","resolution":{"observed_at":"2026-08-06T21:23:57.058905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:56.824235Z","title":"Pico+: Contrastive label disambiguation for robust partial label learning,","venue":null,"work_id":"46fee926-2541-4a72-a49e-aca180aae2c9","year":2023},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:52.013484Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:7369f89205cd623cf52981bc64a6152ffa1325f6596cd6dd801795ade588769e","observation_id":"f4972a17-8290-4357-baa4-0eddff0d4091","resolution":{"observed_at":"2026-08-06T21:23:56.890994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:56.669651Z","title":"Kmt-pll: K-means cross-attention transformer for partial label learning,","venue":null,"work_id":"d046c083-b9b8-4997-a423-f7d2b3d7296c","year":2024},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:52.151662Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:6d695664a7d029d085c0d6217eabdd6f0f585db4b22972057d507f775676f17c","observation_id":"9efc6c75-b4c4-4fbb-8697-cc9d8d86aa0e","resolution":{"observed_at":"2026-08-06T21:23:56.734764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:56.492083Z","title":"Instance-dependent partial label learning,","venue":null,"work_id":"c09c1b44-9b39-4253-be2d-2fb0fa16c8b3","year":2021},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:52.316208Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:0f40221a53487ed3e0516c70acba9fe7b0c42d7d5db82e66751780d408e66b07","observation_id":"68111ade-5759-46ee-85a4-3df56b3abec6","resolution":{"observed_at":"2026-08-06T21:23:56.580052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:56.274769Z","title":"Variational label enhancement for instance-dependent partial label learning,","venue":null,"work_id":"962a5636-65f0-4f4a-a88c-b54409fefb5d","year":2024},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:52.461446Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:9f747269e4f3c88afec95ec72c4bc8f1a715694bb1ba0c3e318f2a8b71e0f242","observation_id":"ed73dab2-ae38-4911-bea4-99b070fbf874","resolution":{"observed_at":"2026-08-06T21:23:56.383003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:56.106276Z","title":"Ambiguity-induced contrastive learning for instance-dependent partial label learning","venue":null,"work_id":"bb30c786-2a23-45a4-8131-adef93fcaeb4","year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:52.666846Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:06f4868fa03b6b8d5f3c05cd56c2b303bad62d382956ca213b00daf35c7b9301","observation_id":"58664e3a-e2ba-45e8-a45b-5caa832cf927","resolution":{"observed_at":"2026-08-06T21:23:56.175655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:55.930806Z","title":"Distilling reliable knowledge for instance-dependent partial label learning,","venue":null,"work_id":"c5a7a3e3-28ed-4a92-a8df-8612cd06fc1b","year":2024},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:52.854905Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:a273ee6f18fbf952f83a07fda914da24047c629fe393aad9edcdf257790ab894","observation_id":"a5129497-87c0-4f67-9314-e8b6e2b54409","resolution":{"observed_at":"2026-08-06T21:23:56.007358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:53.068758Z","title":"Dif-fusion: Toward high color fidelity in infrared and visible image fusion with diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.068758Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:2a109427cfd715c6019017406fd2cbcfa34df9f9f662f9a7bd3414800734ce81","observation_id":"84b42902-ff31-41e7-aa54-eef05635e5e1","resolution":{"observed_at":"2026-08-06T21:23:53.068758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-06T21:23:53.276428Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.276428Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:add7871561300111dc422dcb27e01aad2627fc92cb18a0b73e2c26b07574fd6d","observation_id":"0d679b19-7ebe-4364-ad6d-a2a56a4e7f70","resolution":{"observed_at":"2026-08-06T21:23:53.276428Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:23:53.389322Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.389322Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:5efa6cee1c7977fa0ba9f456e6d3f5e7c2c44401f060965ff74b6bfeccac30fb","observation_id":"a32f3711-3029-4d9c-bfe7-5ee79ced60be","resolution":{"observed_at":"2026-08-06T21:23:53.389322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-06T21:23:53.452979Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.452979Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:e3493d13c1e52a84f0fd18fb61114d3e9b2dc6782f74ae8f200a8a910170c735","observation_id":"e8f05574-b1f9-4ad6-8053-57d4f18d83f9","resolution":{"observed_at":"2026-08-06T21:23:53.452979Z","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":"2026-08-06T21:23:55.775288Z","title":"Label-retrieval-augmented diffusion models for learning from noisy labels,","venue":null,"work_id":"25b9c366-3ff8-4317-8a5f-0e240ff924b7","year":2024},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.535654Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:49e7aec7ebbeb0f7269a10edef4e2b0ac471a0727d3cad663ce10c778564233f","observation_id":"a00689c4-7832-48db-a868-f4bde928b073","resolution":{"observed_at":"2026-08-06T21:23:55.834261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.00308","last_updated":"2022-11-29T07:18:50Z","snapshot_observed_at":"2026-08-03T10:24:17.247396Z","submitted_at":"2022-01-02T06:44:23Z","title":"DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.00308","snapshot_observed_at":"2026-08-06T21:23:53.632174Z","title":"Diffusevae: Efficient, controllable and high-fidelity generation from low-dimensional latents,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.632174Z"},"links":{"cited_paper":"/paper/2201.00308","citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:56af0ef24ac1ac837447aefaf3f3e10e3badd5e41447cbf3ffb389ff9295de3d","observation_id":"73b51962-bfa1-48fc-ace0-e6f3b145afdd","resolution":{"observed_at":"2026-08-06T21:23:53.632174Z","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":"2026-08-06T21:23:55.550586Z","title":"Rank-loss support instance machines for miml instance annotation,","venue":null,"work_id":"f5870651-ba05-47ad-8e90-dc277945826a","year":2012},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.711500Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:feff94391d74e0998cde3516e154dcb7925089c6bf4d382e5c16637487ed3595","observation_id":"1107f0a3-bae7-407d-b02c-3253c3f6c0fb","resolution":{"observed_at":"2026-08-06T21:23:55.658491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:55.389939Z","title":"Learning by associating ambiguously labeled images,","venue":null,"work_id":"70fc4d89-374c-4dd4-b756-0d73af796bd4","year":2013},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.774576Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:0b357b19cf77df65d7af5523aada739ab2456acb077cc4e4c42decd0e805c8a2","observation_id":"0900c96e-3b9f-4d68-b418-a9abfd91fbc4","resolution":{"observed_at":"2026-08-06T21:23:55.472409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:55.262338Z","title":"Multiple instance metric learning from automatically labeled bags of faces,","venue":null,"work_id":"01b821ee-7ddb-4e8e-903c-103cf999fe18","year":2010},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.869362Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:c8e9b463db530263c9f22f82259c8796981c9bdfc62ba70e4b52a10f12ba442d","observation_id":"893ee0f5-b9da-48b0-84a0-050fb156a832","resolution":{"observed_at":"2026-08-06T21:23:55.320549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:53.945003Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:53.945003Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:021e0a404d6a07b1d780ccfe044b4aa02a375290372c44074a3ce6fafbc2cba8","observation_id":"c70df77e-927d-4109-949d-aa67aebd9c17","resolution":{"observed_at":"2026-08-06T21:23:53.945003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01718","last_updated":"2018-12-03T12:37:31Z","snapshot_observed_at":"2026-07-06T07:19:11.957225Z","submitted_at":"2018-12-03T12:37:31Z","title":"Deep Learning for Classical Japanese Literature","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.01718","snapshot_observed_at":"2026-08-06T21:23:54.034729Z","title":"Deep learning for classical japanese literature,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.034729Z"},"links":{"cited_paper":"/paper/1812.01718","citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:9745d78c567c2a578b3ec38c222c7e258a698c085e86aec823879409ea0466bd","observation_id":"2f6472e0-dda9-45c4-a8f1-ae84d878767c","resolution":{"observed_at":"2026-08-06T21:23:54.034729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:23:54.113419Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.113419Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:e03a4f4a9cdcb08f40d25d886b9a470f45be88a80adf4c155465d896a1a3bc1f","observation_id":"e5866dec-a2f3-4c24-b140-b745006ccb25","resolution":{"observed_at":"2026-08-06T21:23:54.113419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-06T21:23:54.238269Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.238269Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:ea8ca0d7735a7015c0fbb8fab3fbb9fe5c36f7dc1cb5d6170f35301e3eed062c","observation_id":"f05eb56d-bf31-47d0-a0bb-7f1007ec70ce","resolution":{"observed_at":"2026-08-06T21:23:54.238269Z","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":"2026-08-06T21:23:55.076665Z","title":"Exploiting class activation value for partial-label learning,","venue":null,"work_id":"6e14117e-077b-404e-ab05-b75a7d304c66","year":2021},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.296452Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:144a10b96372faceb1314248fde1ca28442e212fff60df950a3668fd2b39d0cf","observation_id":"84767bd2-bb74-4f94-bfe1-62fce00d76b1","resolution":{"observed_at":"2026-08-06T21:23:55.160383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:54.937569Z","title":"Revisiting consistency reg- ularization for deep partial label learning,","venue":null,"work_id":"8776e270-c152-4e94-bf70-43c5fc935210","year":2022},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.362480Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:bb75e3475747f26b64d0a0076dd4cf2bc88a87e73be5a5f9c858be2be9542115","observation_id":"bdb9652d-723e-469e-a326-f9868ad70602","resolution":{"observed_at":"2026-08-06T21:23:55.008131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:54.467517Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.467517Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:63762b5f2a24b066ad10ccb7bc39d6f6d2ebd51dacacb05d729bd5c4d3c0efd4","observation_id":"27a6fe3d-70ba-44ce-9521-e65a3bf60d49","resolution":{"observed_at":"2026-08-06T21:23:54.467517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:23:54.538469Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.538469Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:f03e895fc34e260934e33bf5b8260914fdb43a401cfdd0946cb74bebc18838c8","observation_id":"81379066-b9c7-40f1-9ea4-35c3c2ae70c7","resolution":{"observed_at":"2026-08-06T21:23:54.538469Z","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":"2026-08-06T21:23:54.801915Z","title":"Using pre-training can improve model robustness and uncertainty,","venue":null,"work_id":"bf7de94d-eb9e-480a-b5bd-93eb10aa8ec1","year":2019},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.596962Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:a582d27ef3a79d74078fb487bfc1f7b601ac6200d2d18aaa5cebc4219a5317b4","observation_id":"3c56c3a7-409b-47da-91ea-f085b1ac626b","resolution":{"observed_at":"2026-08-06T21:23:54.870240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T21:23:54.659469Z","title":"On calibration of modern neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T21:23:54.659469Z"},"links":{"citing_paper":"/paper/2507.00411"},"observation_digest":"sha256:3e26852f32647865e9220c96c42f14525108dc6fd1f71e36b2d899f8b4f933ae","observation_id":"e8a95152-9337-47ec-b03a-5f8e1857c6a8","resolution":{"observed_at":"2026-08-06T21:23:54.659469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.00411","last_updated":"2026-07-28T01:16:57Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T21:15:11.745611Z","submitted_at":"2025-07-01T03:53:45Z","title":"Diffusion Disambiguation Models for Partial Label Learning"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":35},"total_outbound_references":48},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2507.00411."}