{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:DRYGTAQNAYXMTXLDOUU5XFUUBO","short_pith_number":"pith:DRYGTAQN","canonical_record":{"source":{"id":"2302.09891","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-20T10:39:31Z","cross_cats_sorted":[],"title_canon_sha256":"8b5bf250395ee322ecf4f7fafa9cf1d5c44ef3d54fab37e997107036c07d0fbf","abstract_canon_sha256":"11fb5d2f025d920879d189f30674d734f054637ea070096efd9801bf7f513065"},"schema_version":"1.0"},"canonical_sha256":"1c7069820d062ec9dd637529db96940b8fe5136360c40f3bfb556df2ca27b9d7","source":{"kind":"arxiv","id":"2302.09891","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.09891","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"arxiv_version","alias_value":"2302.09891v2","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.09891","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"pith_short_12","alias_value":"DRYGTAQNAYXM","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"pith_short_16","alias_value":"DRYGTAQNAYXMTXLD","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"pith_short_8","alias_value":"DRYGTAQN","created_at":"2026-07-05T06:45:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:DRYGTAQNAYXMTXLDOUU5XFUUBO","target":"record","payload":{"canonical_record":{"source":{"id":"2302.09891","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-20T10:39:31Z","cross_cats_sorted":[],"title_canon_sha256":"8b5bf250395ee322ecf4f7fafa9cf1d5c44ef3d54fab37e997107036c07d0fbf","abstract_canon_sha256":"11fb5d2f025d920879d189f30674d734f054637ea070096efd9801bf7f513065"},"schema_version":"1.0"},"canonical_sha256":"1c7069820d062ec9dd637529db96940b8fe5136360c40f3bfb556df2ca27b9d7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:45:49.036214Z","signature_b64":"Fx0Jlr2vLzpO/KcQafUraUkRHRKDNfbGPanlenBh6o6MWxhuqCp1A7fe2ZeINa42SVbMd/4NcUscSu20n3x5CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c7069820d062ec9dd637529db96940b8fe5136360c40f3bfb556df2ca27b9d7","last_reissued_at":"2026-07-05T06:45:49.035754Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:45:49.035754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.09891","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:45:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k7AbHD83BePvDxA2NIcMgCh5XF3xayzVtJFc92MKbmQyMalaK0s7RixvmnqvCTIgcfsXtbHVTiPV9+QdSTSfCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:28:11.016334Z"},"content_sha256":"240a4395b39b8501ffd41679bfa12f9e0c5fad384cc14adacfcc60c03d297253","schema_version":"1.0","event_id":"sha256:240a4395b39b8501ffd41679bfa12f9e0c5fad384cc14adacfcc60c03d297253"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:DRYGTAQNAYXMTXLDOUU5XFUUBO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unreliable Partial Label Learning with Recursive Separation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hua Yuan, Ning Xu, Xin Geng, Yu Shi","submitted_at":"2023-02-20T10:39:31Z","abstract_excerpt":"Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-truth label is always among the candidate label set would be unrealistic, as the reliability of the candidate label sets in real-world applications cannot be guaranteed by annotators. Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set. Due to the challenges posed by unreliable labeli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.09891","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.09891/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:45:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FmMy3HvOKkXLVftqrx7QqS88/tImFuzRtMYjZ+lJzLiSLUzLkHoEP20tT6vd4gWWTlj0y5M95KdDfGScczBzDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T18:28:11.016917Z"},"content_sha256":"a3f81586df9a2c307c9fd4c245b2d3fe310f1bfcaa052f82796d9a82548abe01","schema_version":"1.0","event_id":"sha256:a3f81586df9a2c307c9fd4c245b2d3fe310f1bfcaa052f82796d9a82548abe01"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO/bundle.json","state_url":"https://pith.science/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T18:28:11Z","links":{"resolver":"https://pith.science/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO","bundle":"https://pith.science/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO/bundle.json","state":"https://pith.science/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DRYGTAQNAYXMTXLDOUU5XFUUBO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DRYGTAQNAYXMTXLDOUU5XFUUBO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"11fb5d2f025d920879d189f30674d734f054637ea070096efd9801bf7f513065","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-20T10:39:31Z","title_canon_sha256":"8b5bf250395ee322ecf4f7fafa9cf1d5c44ef3d54fab37e997107036c07d0fbf"},"schema_version":"1.0","source":{"id":"2302.09891","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.09891","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"arxiv_version","alias_value":"2302.09891v2","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.09891","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"pith_short_12","alias_value":"DRYGTAQNAYXM","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"pith_short_16","alias_value":"DRYGTAQNAYXMTXLD","created_at":"2026-07-05T06:45:49Z"},{"alias_kind":"pith_short_8","alias_value":"DRYGTAQN","created_at":"2026-07-05T06:45:49Z"}],"graph_snapshots":[{"event_id":"sha256:a3f81586df9a2c307c9fd4c245b2d3fe310f1bfcaa052f82796d9a82548abe01","target":"graph","created_at":"2026-07-05T06:45:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2302.09891/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-truth label is always among the candidate label set would be unrealistic, as the reliability of the candidate label sets in real-world applications cannot be guaranteed by annotators. Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set. Due to the challenges posed by unreliable labeli","authors_text":"Hua Yuan, Ning Xu, Xin Geng, Yu Shi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-20T10:39:31Z","title":"Unreliable Partial Label Learning with Recursive Separation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.09891","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:240a4395b39b8501ffd41679bfa12f9e0c5fad384cc14adacfcc60c03d297253","target":"record","created_at":"2026-07-05T06:45:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"11fb5d2f025d920879d189f30674d734f054637ea070096efd9801bf7f513065","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-02-20T10:39:31Z","title_canon_sha256":"8b5bf250395ee322ecf4f7fafa9cf1d5c44ef3d54fab37e997107036c07d0fbf"},"schema_version":"1.0","source":{"id":"2302.09891","kind":"arxiv","version":2}},"canonical_sha256":"1c7069820d062ec9dd637529db96940b8fe5136360c40f3bfb556df2ca27b9d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c7069820d062ec9dd637529db96940b8fe5136360c40f3bfb556df2ca27b9d7","first_computed_at":"2026-07-05T06:45:49.035754Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:45:49.035754Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fx0Jlr2vLzpO/KcQafUraUkRHRKDNfbGPanlenBh6o6MWxhuqCp1A7fe2ZeINa42SVbMd/4NcUscSu20n3x5CA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:45:49.036214Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.09891","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:240a4395b39b8501ffd41679bfa12f9e0c5fad384cc14adacfcc60c03d297253","sha256:a3f81586df9a2c307c9fd4c245b2d3fe310f1bfcaa052f82796d9a82548abe01"],"state_sha256":"5345ca71c5eeef24f73dc78db237caf2400af0265a5879be6e07c6ddd23370fc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MO4XEHxm6hWMF8idSVMIyeZEF2MwK6/B+SnrztlcKnuECe45g0SVSGj8ca90MwtyJFVA7QB5yWyX10T2XF3MCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T18:28:11.020901Z","bundle_sha256":"863ad083e069019c33c1fd7a9e5031305f92b07c8904daabd5b25089a6dd33e9"}}