{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NF6REOSSNOHAK25CTBZ7AUHD6W","short_pith_number":"pith:NF6REOSS","canonical_record":{"source":{"id":"2401.10458","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-19T02:16:30Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"02b95c6c31c0037d3c204daba460ceaedbd3dfa1d46a08e99822a62628d88740","abstract_canon_sha256":"1be27063b848a37c9805713864ce7211ecc57efcf33c77332e72a4ad54dfad34"},"schema_version":"1.0"},"canonical_sha256":"697d123a526b8e056ba29873f050e3f599cd21140f67f3e68f83cd2b82018f3b","source":{"kind":"arxiv","id":"2401.10458","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.10458","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"arxiv_version","alias_value":"2401.10458v1","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10458","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"pith_short_12","alias_value":"NF6REOSSNOHA","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"pith_short_16","alias_value":"NF6REOSSNOHAK25C","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"pith_short_8","alias_value":"NF6REOSS","created_at":"2026-07-05T07:35:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NF6REOSSNOHAK25CTBZ7AUHD6W","target":"record","payload":{"canonical_record":{"source":{"id":"2401.10458","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-19T02:16:30Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"02b95c6c31c0037d3c204daba460ceaedbd3dfa1d46a08e99822a62628d88740","abstract_canon_sha256":"1be27063b848a37c9805713864ce7211ecc57efcf33c77332e72a4ad54dfad34"},"schema_version":"1.0"},"canonical_sha256":"697d123a526b8e056ba29873f050e3f599cd21140f67f3e68f83cd2b82018f3b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:26.608945Z","signature_b64":"SkXBZOm37F4SkTfYoLWoamNYkK9EVjkN2CZ2zxwbTTn2EXxfxXb7XovfOmXNM61AhKubFHikRbjK1RcWFUGaBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"697d123a526b8e056ba29873f050e3f599cd21140f67f3e68f83cd2b82018f3b","last_reissued_at":"2026-07-05T07:35:26.608427Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:26.608427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.10458","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:35:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3IeEwwvcbc/FwRx4CMg/4nE+wkdVbpHTLSnhxYPc/XxpSTShPta3CNJh2fiOPj0hkyi/Vqwwr+igULt6cGGoCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T17:39:54.497243Z"},"content_sha256":"752c560ed8d5ae1c61c959f395c3eaa243bc0403b3a34b2baea31d48941fc584","schema_version":"1.0","event_id":"sha256:752c560ed8d5ae1c61c959f395c3eaa243bc0403b3a34b2baea31d48941fc584"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NF6REOSSNOHAK25CTBZ7AUHD6W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contrastive Unlearning: A Contrastive Approach to Machine Unlearning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Carl Yang, Hong kyu Lee, Jian Lou, Li Xiong, Qiuchen Zhang","submitted_at":"2024-01-19T02:16:30Z","abstract_excerpt":"Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearning samples without negatively impacting the overall model performance is still challenging. In this paper, we propose a contrastive unlearning framework, leveraging the concept of representation learning for more effective unlearning. It removes the influence of unlearning samples by contrasting their embeddings against the remaining samples so that they are pushed away from their original classes and pulled toward "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10458","kind":"arxiv","version":1},"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/2401.10458/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":"2026-07-05T07:35:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xgs4ScnALCflGCGDxR+R5rSRHT4DmjQdKOuxm6JsGXNASFwg4BoICimnaM0lCrOxahfAQAcmNJMAY3l/RlNMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T17:39:54.497754Z"},"content_sha256":"b00c4395993c3ffb929e6ab42f63426dc5c9af18619e56aad6a8d127db67c920","schema_version":"1.0","event_id":"sha256:b00c4395993c3ffb929e6ab42f63426dc5c9af18619e56aad6a8d127db67c920"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NF6REOSSNOHAK25CTBZ7AUHD6W/bundle.json","state_url":"https://pith.science/pith/NF6REOSSNOHAK25CTBZ7AUHD6W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NF6REOSSNOHAK25CTBZ7AUHD6W/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":"2026-08-11T17:39:54Z","links":{"resolver":"https://pith.science/pith/NF6REOSSNOHAK25CTBZ7AUHD6W","bundle":"https://pith.science/pith/NF6REOSSNOHAK25CTBZ7AUHD6W/bundle.json","state":"https://pith.science/pith/NF6REOSSNOHAK25CTBZ7AUHD6W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NF6REOSSNOHAK25CTBZ7AUHD6W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NF6REOSSNOHAK25CTBZ7AUHD6W","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":"1be27063b848a37c9805713864ce7211ecc57efcf33c77332e72a4ad54dfad34","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-19T02:16:30Z","title_canon_sha256":"02b95c6c31c0037d3c204daba460ceaedbd3dfa1d46a08e99822a62628d88740"},"schema_version":"1.0","source":{"id":"2401.10458","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.10458","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"arxiv_version","alias_value":"2401.10458v1","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10458","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"pith_short_12","alias_value":"NF6REOSSNOHA","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"pith_short_16","alias_value":"NF6REOSSNOHAK25C","created_at":"2026-07-05T07:35:26Z"},{"alias_kind":"pith_short_8","alias_value":"NF6REOSS","created_at":"2026-07-05T07:35:26Z"}],"graph_snapshots":[{"event_id":"sha256:b00c4395993c3ffb929e6ab42f63426dc5c9af18619e56aad6a8d127db67c920","target":"graph","created_at":"2026-07-05T07:35:26Z","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/2401.10458/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearning samples without negatively impacting the overall model performance is still challenging. In this paper, we propose a contrastive unlearning framework, leveraging the concept of representation learning for more effective unlearning. It removes the influence of unlearning samples by contrasting their embeddings against the remaining samples so that they are pushed away from their original classes and pulled toward ","authors_text":"Carl Yang, Hong kyu Lee, Jian Lou, Li Xiong, Qiuchen Zhang","cross_cats":["cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-19T02:16:30Z","title":"Contrastive Unlearning: A Contrastive Approach to Machine Unlearning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10458","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":"sha256:752c560ed8d5ae1c61c959f395c3eaa243bc0403b3a34b2baea31d48941fc584","target":"record","created_at":"2026-07-05T07:35:26Z","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":"1be27063b848a37c9805713864ce7211ecc57efcf33c77332e72a4ad54dfad34","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-19T02:16:30Z","title_canon_sha256":"02b95c6c31c0037d3c204daba460ceaedbd3dfa1d46a08e99822a62628d88740"},"schema_version":"1.0","source":{"id":"2401.10458","kind":"arxiv","version":1}},"canonical_sha256":"697d123a526b8e056ba29873f050e3f599cd21140f67f3e68f83cd2b82018f3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"697d123a526b8e056ba29873f050e3f599cd21140f67f3e68f83cd2b82018f3b","first_computed_at":"2026-07-05T07:35:26.608427Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:35:26.608427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SkXBZOm37F4SkTfYoLWoamNYkK9EVjkN2CZ2zxwbTTn2EXxfxXb7XovfOmXNM61AhKubFHikRbjK1RcWFUGaBA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:35:26.608945Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.10458","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:752c560ed8d5ae1c61c959f395c3eaa243bc0403b3a34b2baea31d48941fc584","sha256:b00c4395993c3ffb929e6ab42f63426dc5c9af18619e56aad6a8d127db67c920"],"state_sha256":"272f2c1148616e6e60dee6a8ade4abc19699d6c13255e6f6f149b25fb2eb7112"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HaA17N0tgIXG6PkiE20AQYuboMnR80ESAcTv09pfF4MpVk7FD9sHmvZUBvYKOUigJBnAVKLUrorCa5YwKQrrDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T17:39:54.503975Z","bundle_sha256":"b5082a8150495303ba7baaa8ad1f9c4fdadc07a480be2d6cf05211a591b256e7"}}