{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AM4RB2TKAXOCZMYKT4TA5D7WSC","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":"20ddc4cd2da996d40baea13149ae2a11e4b09cbcc41bc08b6d3445c8408d63b2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-24T01:45:13Z","title_canon_sha256":"a603fe4ccba862ab16bda38fb346d92fa519eeec3a3c6f7aa55fcd49e5b45988"},"schema_version":"1.0","source":{"id":"2406.16257","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.16257","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"arxiv_version","alias_value":"2406.16257v3","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.16257","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_12","alias_value":"AM4RB2TKAXOC","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_16","alias_value":"AM4RB2TKAXOCZMYK","created_at":"2026-07-05T10:22:47Z"},{"alias_kind":"pith_short_8","alias_value":"AM4RB2TK","created_at":"2026-07-05T10:22:47Z"}],"graph_snapshots":[{"event_id":"sha256:3a8d681baa1eddc17ea3247bb7a02ac81dba44662578c0394e93089f9456dd7b","target":"graph","created_at":"2026-07-05T10:22:47Z","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/2406.16257/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine unlearning is the process of efficiently removing the influence of a training data instance from a trained machine learning model without retraining it from scratch. A popular subclass of unlearning approaches is exact machine unlearning, which focuses on techniques that explicitly guarantee the removal of the influence of a data instance from a model. Exact unlearning approaches use a machine learning model in which individual components are trained on disjoint subsets of the data. During deletion, exact unlearning approaches only retrain the affected components rather than the entire","authors_text":"Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Snigdha Chaturvedi, Somnath Basu Roy Chowdhury","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-24T01:45:13Z","title":"Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.16257","kind":"arxiv","version":3},"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:e495bb4bf35bf3ac2c9815ca331594fca4bc9137b18a11634a89a52d56958ca9","target":"record","created_at":"2026-07-05T10:22:47Z","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":"20ddc4cd2da996d40baea13149ae2a11e4b09cbcc41bc08b6d3445c8408d63b2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-24T01:45:13Z","title_canon_sha256":"a603fe4ccba862ab16bda38fb346d92fa519eeec3a3c6f7aa55fcd49e5b45988"},"schema_version":"1.0","source":{"id":"2406.16257","kind":"arxiv","version":3}},"canonical_sha256":"033910ea6a05dc2cb30a9f260e8ff69090c2417050a6c4445aaefe82944c38ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"033910ea6a05dc2cb30a9f260e8ff69090c2417050a6c4445aaefe82944c38ca","first_computed_at":"2026-07-05T10:22:47.556633Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:22:47.556633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i5oa8RcwjPywz8rjPUsK2XOhn8qln9QKnU6OsintyqerHdcZfkmtOuPZfIGtMBWqAPQDgC8x/xjXzBYXz7zwBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:22:47.557449Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.16257","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e495bb4bf35bf3ac2c9815ca331594fca4bc9137b18a11634a89a52d56958ca9","sha256:3a8d681baa1eddc17ea3247bb7a02ac81dba44662578c0394e93089f9456dd7b"],"state_sha256":"ab94dca1b706960c62a2e742a6e3a083d6b1fa44c25c7314c3ef4a82dcb8e4a7"}