{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:DANRMDXQNABT2RHU23WZAPDDCB","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":"d1ded7e1bb7574341c37d4cd827667a45d9c38932561bb22a8f751ae5bc7671a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","title_canon_sha256":"f91a34aef2bd0bab0fa96b55f0becfe9906f072105473f4a9e029d53deb04681"},"schema_version":"1.0","source":{"id":"2606.07086","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07086","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07086v1","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07086","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_12","alias_value":"DANRMDXQNABT","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_16","alias_value":"DANRMDXQNABT2RHU","created_at":"2026-06-08T01:04:45Z"},{"alias_kind":"pith_short_8","alias_value":"DANRMDXQ","created_at":"2026-06-08T01:04:45Z"}],"graph_snapshots":[{"event_id":"sha256:84f12235b99a0b41aff41ba94d34199e9f68e0e30a8c7a4f211c8016d9ad638f","target":"graph","created_at":"2026-06-08T01:04:45Z","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/2606.07086/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupted by ambiguity, human error, or dynamic environments. Over-parameterized DNNs easily memorize these noisy labels during training, degrading model accuracy and generalization. Existing data-cleaning and sample-selection strategies often rely on manually specified thresholds, prior knowledge of the noise ratio, or a single metric (either learning dynamics or geometric structure), making them unstable in complex data regimes. This paper proposes ","authors_text":"Chen-Hsuan Fang, Jung-Hua Wang, Pin-Hsuan Yu, Tsung-Wei Pan, Wei-Hsinag Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","title":"An Adaptive Data cleaning Framework for Noisy Label Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07086","kind":"arxiv","version":1},"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":"sha256:70487eb0af579ce7b1d7c912b41d6fe736e81519435ce5118558ec22d1974568","target":"record","created_at":"2026-06-08T01:04:45Z","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":"d1ded7e1bb7574341c37d4cd827667a45d9c38932561bb22a8f751ae5bc7671a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T09:32:18Z","title_canon_sha256":"f91a34aef2bd0bab0fa96b55f0becfe9906f072105473f4a9e029d53deb04681"},"schema_version":"1.0","source":{"id":"2606.07086","kind":"arxiv","version":1}},"canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"181b160ef068033d44f4d6ed903c63107fccec72d610b34efc43a796cb1c93a0","first_computed_at":"2026-06-08T01:04:45.474804Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:45.474804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yfr9xw2RSQgLvbzGOKl3Y0pBCwF8q4s5XXT5AIrXSACWR17czVF7/E2oijTE63BKmaWB0bkVb17lIxpQlJkzCg==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:45.475623Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07086","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70487eb0af579ce7b1d7c912b41d6fe736e81519435ce5118558ec22d1974568","sha256:84f12235b99a0b41aff41ba94d34199e9f68e0e30a8c7a4f211c8016d9ad638f"],"state_sha256":"354acd838f93ef17498e9c620780180417da3b50450a138a66f7018ed25d3cdf"}