{"as_of":"2026-08-07T23:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:33b5cb0a8d3c1c038096734919ba54ffec84e6170e784cbd91f77356eb38cd4b","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T22:51:44.522239Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"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/2508.06346/citation-record","integrity":"/paper/2508.06346/integrity","json":"/paper/2508.06346/citation-record.json","paper":"/paper/2508.06346"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T22:51:44.802904Z","title":"Natarajan, I","venue":null,"work_id":"1ae5017e-2635-4801-ab90-b35ec40c7f43","year":2013},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.443179Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:613156c01fdf3fb6f9602921d2ce8844a6395fe71d11cd089312f2e9a95f6214","observation_id":"286578b6-964f-4e04-a077-cf975ccd97b8","resolution":{"observed_at":"2026-08-05T22:51:44.805459Z","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":"2026-08-05T22:51:44.795874Z","title":null,"venue":null,"work_id":"956a302a-6206-41d2-86b4-79996bce4820","year":2023},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.445998Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:28f1a57c34e658e80b2ed1c5b33ebce07e016c6bfdf95f4188c0824eb7f3d0ca","observation_id":"86f0de28-92b7-4d47-b818-414a5368f2df","resolution":{"observed_at":"2026-08-05T22:51:44.798250Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.788567Z","title":null,"venue":null,"work_id":"db6416a2-61e4-4aa5-aa1d-9f2f910fd0dd","year":null},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.448476Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:f041aeaccd3e44be26f3aa9857a8e7ccbf66e92e10e48fca2aacc9a05bf998a0","observation_id":"4d746b9b-8b2f-4a27-a134-614ac499bbe7","resolution":{"observed_at":"2026-08-05T22:51:44.791144Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.781253Z","title":"Zamora, A","venue":null,"work_id":"be7e5cec-d758-45d5-8795-26418d540359","year":2022},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.450845Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:3200fce4619613962ce74e42818a7b985f3441390f9386bbefd2b64f92688ce5","observation_id":"fbb0e8fa-c825-49e8-97f8-0e8edac99341","resolution":{"observed_at":"2026-08-05T22:51:44.783726Z","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":"2026-08-05T22:51:44.773741Z","title":"Kumar, U","venue":null,"work_id":"7ba1fda8-f8ff-4a84-8901-ff0130221582","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.453415Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:c8f3d9916c1f57160c7f4ae8b7df94f0664e827c971efb6fb215e25b73c2283f","observation_id":"63dc739e-c66f-453e-94d6-3cc20b8e867e","resolution":{"observed_at":"2026-08-05T22:51:44.776255Z","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":"2026-08-05T22:51:44.766299Z","title":null,"venue":null,"work_id":"44376c2e-e999-4419-b15f-5d6bf0573371","year":2018},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.455822Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:f522f06795be1ce26077ecc13552aae122d7d10e269b78a2499bc5a452396640","observation_id":"9e7cc1dd-f386-4508-8c07-16e2319e2bb1","resolution":{"observed_at":"2026-08-05T22:51:44.768926Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.758909Z","title":null,"venue":null,"work_id":"2ce09f06-490a-46b0-be65-b67b601ab9ba","year":2025},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.458462Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:826e0f30ef78711307db70952a30d135db7994b9925e8f88877b195480de1c59","observation_id":"8d744f7d-9bd7-49e1-8c65-7f8e4b654b5b","resolution":{"observed_at":"2026-08-05T22:51:44.761324Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.751696Z","title":null,"venue":null,"work_id":"8a62a015-151f-465f-9741-19ff252042c5","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.460949Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:b9c5ed1b77e6a22505d6e5681839c9f7ce72b29da4668f7e8ea98b42165618ae","observation_id":"956634ff-8c3d-4b24-b224-8288de193191","resolution":{"observed_at":"2026-08-05T22:51:44.754103Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.743780Z","title":null,"venue":null,"work_id":"475cdffd-aafb-4027-9741-e5ac8b3d1f00","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.463270Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:5f3cd457237d0fb2f858271215d4d3ee36b5fc5de6ac062e4f9251be55c760aa","observation_id":"dc455c8a-8fd3-4987-a243-0ca9a875c7ae","resolution":{"observed_at":"2026-08-05T22:51:44.746273Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.736698Z","title":"Patrini, A","venue":null,"work_id":"e74def62-b65e-4fe8-98a1-f95765b56c61","year":2017},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.465562Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:37d5f3405459addec6592f3278556a99f9c2bfb97527feaf76270d9a48bf28c1","observation_id":"0af41e70-9a07-44ef-a3ac-444cf1b9eb08","resolution":{"observed_at":"2026-08-05T22:51:44.739093Z","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":"2026-08-05T22:51:44.729602Z","title":null,"venue":null,"work_id":"30f8dde4-b78c-4db4-bfbc-1db16e30529d","year":2025},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.467847Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:c86e663a10311ba13eb4e29da11d98a5268c24529890215bfc614cb962dec496","observation_id":"8e9a1075-31e2-4a89-afaa-ceec87ba57d7","resolution":{"observed_at":"2026-08-05T22:51:44.731968Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.722451Z","title":null,"venue":null,"work_id":"64dfd90b-9a16-467c-bdb8-f4d35f102ba4","year":2023},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.470089Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:fd9735e3e44fb8c081b91270819cad92c7ea50d4daf071463517f69b2315383e","observation_id":"7851624e-529d-464e-acc7-85cd30d08205","resolution":{"observed_at":"2026-08-05T22:51:44.724769Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.714972Z","title":null,"venue":null,"work_id":"72682d04-b95a-4dec-a246-9d8512c71e70","year":null},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.472457Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:b8c5ce4f7e9136420557837bd1afd9eb85091c56cc786e05fbeaaaf9066e14c1","observation_id":"1cc3bdcc-e79a-4acb-9288-4b8b62caab53","resolution":{"observed_at":"2026-08-05T22:51:44.717495Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.707406Z","title":null,"venue":null,"work_id":"2de2bd36-ddb6-4fff-8d12-68eaac6f8676","year":2015},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.474799Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:a3c763b6909d5a2bb445003e1be379e4fdf6c2f718bd78b08716b6e2677c5586","observation_id":"07ecae90-60dd-4094-8ccb-73db7bf73254","resolution":{"observed_at":"2026-08-05T22:51:44.710078Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.700362Z","title":"Zheng, A","venue":null,"work_id":"b073144d-04bf-4faa-ab81-0fd9a138830f","year":2021},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.477142Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:9eaa6016f9d6d7e258441a2fb2d2789e2f529c2057c7785ee2cd9ba9f65b3990","observation_id":"53b806ed-96ee-4285-87b3-e2b2517acb06","resolution":{"observed_at":"2026-08-05T22:51:44.702626Z","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":"2026-08-05T22:51:44.692651Z","title":null,"venue":null,"work_id":"36cd2435-9b4a-40c4-8c3d-9570e9637ca5","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.479248Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:c49e2803995e6f1f036c2f0548fbceb84da6915bd2b8c1f4ec65801cea4304dc","observation_id":"6685aab3-5360-4fe5-827e-8c293557dce5","resolution":{"observed_at":"2026-08-05T22:51:44.695087Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.685462Z","title":null,"venue":null,"work_id":"31179455-d488-428d-9264-009ae7185dcf","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.481439Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:47363ba0cdba706fb0bee42ebddcfcbe299cbb2d57ffb717b6188af60cdaeeb3","observation_id":"34199304-3969-423a-ba49-31396eb4aaa1","resolution":{"observed_at":"2026-08-05T22:51:44.687765Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.678260Z","title":"Englesson, H","venue":null,"work_id":"76541e39-6bad-4445-8424-c24d1c598415","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.483612Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:9032df5d203f1dc2a83c7928c26383bb1cf10e471f83de528661fa04a4cf0d3f","observation_id":"e5186767-c8e0-4485-8672-5a3f8595a613","resolution":{"observed_at":"2026-08-05T22:51:44.680797Z","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":"2026-08-05T22:51:44.671376Z","title":"Jiang, Z","venue":null,"work_id":"4963439f-aad4-4028-871b-6ed850e04abb","year":2018},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.485825Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:b53bbd3c1cbabaf0f97d40c9ffaa5d539e0fa35bb716a0f33e67bd157da04ee5","observation_id":"ecc0e792-204b-42fc-a715-c34961a22163","resolution":{"observed_at":"2026-08-05T22:51:44.673679Z","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":"2026-08-05T22:51:44.663950Z","title":null,"venue":null,"work_id":"e5fd5af4-0998-4da6-9488-d03ddfbaa02a","year":2021},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.488122Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:8470bdf776c182710e4d79ac5d20912476e2dd8aecc1d2576f598a3b44665f1d","observation_id":"b8a20da3-673b-4032-b14f-55a9b02fd169","resolution":{"observed_at":"2026-08-05T22:51:44.666428Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.655150Z","title":null,"venue":null,"work_id":"1a26962c-4282-44ad-9b9e-b007e317bfb1","year":2019},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.490409Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:d918c62b1b9f7f9de6e34e33ada86f8c425497cb8d081e5203b9b97bbdf5f129","observation_id":"ca9c4ff3-3e7d-4516-b46f-724d33081fd0","resolution":{"observed_at":"2026-08-05T22:51:44.657855Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2403.01942","last_updated":"2024-08-29T05:48:42Z","snapshot_observed_at":"2026-07-06T17:39:07.926765Z","submitted_at":"2024-03-04T11:24:51Z","title":"Mitigating Label Noise on Graph via Topological Sample Selection","version":4},"cited_work":{"arxiv_id":"2403.01942","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.01942","snapshot_observed_at":"2026-08-05T22:51:44.544181Z","title":"Mitigating Label Noise on Graph via Topological Sample Selection","venue":"cs.LG","work_id":"6aac5ade-1222-4372-8200-1ad2574dcc59","year":2024},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.492581Z"},"links":{"cited_paper":"/paper/2403.01942","citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:abfc740615ec0a187907dfeeb2babeee69b601994fe95e3a6a82ff8b046d45f7","observation_id":"bf392996-f4d1-41c3-bcc8-2337175d128e","resolution":{"observed_at":"2026-08-05T22:51:44.549229Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-08-05T22:51:44.647016Z","title":null,"venue":null,"work_id":"21a41146-da44-4118-beb7-001bcc6a5357","year":2018},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.495395Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:8e6f9e17ae6ebc3febf6fdf627237d54f2eedabc353aa6a174b88fab811e0b41","observation_id":"8db53591-7e72-4a24-b971-f713f8557d1a","resolution":{"observed_at":"2026-08-05T22:51:44.649599Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.639224Z","title":null,"venue":null,"work_id":"66ea542e-2e6f-4aff-91a1-1fa7006538a3","year":2020},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.497516Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:deaedc81611df5ced24b796802ddcfef3d0474bd857bb353a5f0d9cccdec2964","observation_id":"821aa295-934c-4978-9178-a58dd7aef2c2","resolution":{"observed_at":"2026-08-05T22:51:44.641689Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.631867Z","title":"Tanaka, D","venue":null,"work_id":"81c7d541-e767-4a3a-916f-18bd7471d84a","year":2018},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.499647Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:b6d65877876b89cf8083bcd71482a8e4b28ca7fce796bef1726e9c9df0e81522","observation_id":"e281aa0c-075b-4c64-8b79-7f8ea5f583b7","resolution":{"observed_at":"2026-08-05T22:51:44.634355Z","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":"2026-08-05T22:51:44.624554Z","title":"Ghosh, H","venue":null,"work_id":"510a73f4-6f2a-4897-9bc8-4763c5e8d347","year":2017},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.502012Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:037e71293579c6f8e4897a39bb10e8972305deadb23a6695aec7759fac1d201e","observation_id":"a7a21984-9df3-4d3e-bfd4-237cbb952c9e","resolution":{"observed_at":"2026-08-05T22:51:44.626933Z","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":"2026-08-05T22:51:44.617467Z","title":null,"venue":null,"work_id":"366cab6d-4f01-4a83-966a-dd9b4d2e98c1","year":2020},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.504176Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:9e68ea2d88222a915af627b4810fe6aa5963e1e9f842dc571e8136c90c7130e6","observation_id":"abafc396-2d6c-41f0-8d01-e7c6ce01495d","resolution":{"observed_at":"2026-08-05T22:51:44.619831Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.609812Z","title":null,"venue":null,"work_id":"fbac74c6-371e-4575-ab5d-ed6805661630","year":2021},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.506478Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:3336536eb0cb1120371e4330121545b513bf9b3b6b49a8fb0906da606ee9040f","observation_id":"7af4a05b-7595-4ffd-9649-cdaf6c625d02","resolution":{"observed_at":"2026-08-05T22:51:44.612595Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.601761Z","title":null,"venue":null,"work_id":"efa6d09f-10d0-405f-b675-09e27627aa4d","year":2023},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.508792Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:defa14e60373d77eb604ed7e09cf1b967e7178772fb728ffda0c539f29871bb1","observation_id":"665ce2f8-b3c7-410d-a735-d8fafacbc6e2","resolution":{"observed_at":"2026-08-05T22:51:44.604397Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.594188Z","title":"Zhang, M","venue":null,"work_id":"c0d8e9c8-62bc-4d18-995d-8b0fc4ee40c4","year":2018},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.511044Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:2d3696831b03168ebce9a1b9d442af3dca13fab351b5d771aae11ea3e2a6ced6","observation_id":"d7310eaa-3bda-46bb-ad14-862cbb997328","resolution":{"observed_at":"2026-08-05T22:51:44.596623Z","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":"2026-08-05T22:51:44.585665Z","title":null,"venue":null,"work_id":"6536bad0-4486-412d-bf94-dbbc501fcca6","year":2019},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.513174Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:855de3a74204ce83232c20c3b834501b3be9af4d926f28721e4d791ac016e536","observation_id":"1f9bbb38-03cd-4d9a-9a1f-347a4e10c400","resolution":{"observed_at":"2026-08-05T22:51:44.588332Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.578188Z","title":"Englesson, H","venue":null,"work_id":"dd5a652b-b058-41af-ac86-fca1210da18e","year":2021},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.515418Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:f61861f9bc7d2bbc15fe8101e9ed780eccf4e8da52e465ea3bbb3d66a4c1627a","observation_id":"bf32472d-842a-49c1-8706-50130de71741","resolution":{"observed_at":"2026-08-05T22:51:44.580646Z","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":"2026-08-05T22:51:44.570713Z","title":null,"venue":null,"work_id":"ba258588-e59a-4954-bb7f-49e61673b264","year":2023},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.517675Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:fd0194881ac8f653f1f7aad137b485953b89c7bf41c77a7ed68dbce5fe471662","observation_id":"86436248-339a-4f67-8e4a-a0e59ab5464c","resolution":{"observed_at":"2026-08-05T22:51:44.573214Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.562894Z","title":null,"venue":null,"work_id":"0da301b6-1242-459b-827e-03591fb87c65","year":2018},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.519919Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:79ca94b1c66a698ddc75fe827d12f627862c81e067003888bfef3b0cf7ab8734","observation_id":"85de6bdd-54d5-4cb1-b85b-15e39f2e5638","resolution":{"observed_at":"2026-08-05T22:51:44.565347Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-05T22:51:44.554948Z","title":null,"venue":null,"work_id":"bc19621f-a7af-4eae-a8b0-af5e8e978e6a","year":1993},"citing_paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T22:51:44.522239Z"},"links":{"citing_paper":"/paper/2508.06346"},"observation_digest":"sha256:ae517236f210795f8b7a2dfa6f0ea01dc1091a8e6c6aaa110e056012b5a233bf","observation_id":"2d5f664c-5374-4636-b8b1-b89a8b683919","resolution":{"observed_at":"2026-08-05T22:51:44.557606Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}}],"paper":{"arxiv_id":"2508.06346","last_updated":"2025-08-08T14:20:52Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T22:51:43.210990Z","submitted_at":"2025-08-08T14:20:52Z","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":1,"verified_fuzzy":11},"total_outbound_references":35},"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 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2508.06346."}