{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:RR5ZGTKKI3UWJPY46KVVRLZF4K","short_pith_number":"pith:RR5ZGTKK","canonical_record":{"source":{"id":"2601.06163","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-07T00:13:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"558b578fc7d61f83778c164cac1acc455e022774f80d765bc0b70ba54526b30f","abstract_canon_sha256":"17591eb824d5c682a9af86e32a0b7b2cb220caa82bd637910393a3d5b4e0c783"},"schema_version":"1.0"},"canonical_sha256":"8c7b934d4a46e964bf1cf2ab58af25e286299deed52324dec85d6c9ad61a2fe2","source":{"kind":"arxiv","id":"2601.06163","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.06163","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"arxiv_version","alias_value":"2601.06163v2","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.06163","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"pith_short_12","alias_value":"RR5ZGTKKI3UW","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"pith_short_16","alias_value":"RR5ZGTKKI3UWJPY4","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"pith_short_8","alias_value":"RR5ZGTKK","created_at":"2026-05-20T00:04:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:RR5ZGTKKI3UWJPY46KVVRLZF4K","target":"record","payload":{"canonical_record":{"source":{"id":"2601.06163","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-07T00:13:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"558b578fc7d61f83778c164cac1acc455e022774f80d765bc0b70ba54526b30f","abstract_canon_sha256":"17591eb824d5c682a9af86e32a0b7b2cb220caa82bd637910393a3d5b4e0c783"},"schema_version":"1.0"},"canonical_sha256":"8c7b934d4a46e964bf1cf2ab58af25e286299deed52324dec85d6c9ad61a2fe2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:20.837805Z","signature_b64":"LkU0Vuf1Rqy/qRyxAlepBWLIzaDw/+36qvFJHewA3EACe7exggNcS72nOiKHdGLEVSlkFGjIoLDc1/P17yMuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c7b934d4a46e964bf1cf2ab58af25e286299deed52324dec85d6c9ad61a2fe2","last_reissued_at":"2026-05-20T00:04:20.836966Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:20.836966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.06163","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":"2026-05-20T00:04:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/cEcAg4h2J6qNLlTth2I5kDc105WX69syygwB3GuLvhBb1PePXwxNSwFbx4XWHOr3pwXpJYA4gt/6elqZWhWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T17:07:39.811790Z"},"content_sha256":"17556258d890c5e869627d3ebc8e112f96a0f5a72dabc27b15b76e9beea17d53","schema_version":"1.0","event_id":"sha256:17556258d890c5e869627d3ebc8e112f96a0f5a72dabc27b15b76e9beea17d53"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:RR5ZGTKKI3UWJPY46KVVRLZF4K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Bo Hui, Geng Yuan, Gen Li, Jie Ji, Kaiyuan Deng, Minghai Qin, Xiaolong Ma","submitted_at":"2026-01-07T00:13:36Z","abstract_excerpt":"The widespread adoption of text-to-image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or sensitive imagery. As a practical solution, machine unlearning aims to erase unwanted concepts without retraining from scratch. While most existing methods are effective for single-concept unlearning, they often struggle when removing multiple concepts, causing significant challenges in unlearning effectiveness, generation quality, and sensitivity to hyperparameters and datasets. We take a unique perspective on multi-concept unlearning by leveragi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.06163","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/2601.06163/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":"2026-05-20T00:04:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q7pC3orlguGCs1HAVLLOQlooAQcZtAC1je06cC6WpmUCavVdNKi+qq4AXhZKxmS0eGrJ8OhV7KuABcCMM6QjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T17:07:39.812579Z"},"content_sha256":"7fc2cb153ae57f098b0e423361a6ba51b1e1486bb32b3ebc40b793411d5c6135","schema_version":"1.0","event_id":"sha256:7fc2cb153ae57f098b0e423361a6ba51b1e1486bb32b3ebc40b793411d5c6135"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K/bundle.json","state_url":"https://pith.science/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K/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":"2026-05-29T17:07:39Z","links":{"resolver":"https://pith.science/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K","bundle":"https://pith.science/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K/bundle.json","state":"https://pith.science/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RR5ZGTKKI3UWJPY46KVVRLZF4K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RR5ZGTKKI3UWJPY46KVVRLZF4K","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":"17591eb824d5c682a9af86e32a0b7b2cb220caa82bd637910393a3d5b4e0c783","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-07T00:13:36Z","title_canon_sha256":"558b578fc7d61f83778c164cac1acc455e022774f80d765bc0b70ba54526b30f"},"schema_version":"1.0","source":{"id":"2601.06163","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.06163","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"arxiv_version","alias_value":"2601.06163v2","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.06163","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"pith_short_12","alias_value":"RR5ZGTKKI3UW","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"pith_short_16","alias_value":"RR5ZGTKKI3UWJPY4","created_at":"2026-05-20T00:04:20Z"},{"alias_kind":"pith_short_8","alias_value":"RR5ZGTKK","created_at":"2026-05-20T00:04:20Z"}],"graph_snapshots":[{"event_id":"sha256:7fc2cb153ae57f098b0e423361a6ba51b1e1486bb32b3ebc40b793411d5c6135","target":"graph","created_at":"2026-05-20T00:04:20Z","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/2601.06163/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The widespread adoption of text-to-image (T2I) diffusion models has raised concerns about their potential to generate copyrighted, inappropriate, or sensitive imagery. As a practical solution, machine unlearning aims to erase unwanted concepts without retraining from scratch. While most existing methods are effective for single-concept unlearning, they often struggle when removing multiple concepts, causing significant challenges in unlearning effectiveness, generation quality, and sensitivity to hyperparameters and datasets. We take a unique perspective on multi-concept unlearning by leveragi","authors_text":"Bo Hui, Geng Yuan, Gen Li, Jie Ji, Kaiyuan Deng, Minghai Qin, Xiaolong Ma","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-07T00:13:36Z","title":"Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.06163","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":"sha256:17556258d890c5e869627d3ebc8e112f96a0f5a72dabc27b15b76e9beea17d53","target":"record","created_at":"2026-05-20T00:04:20Z","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":"17591eb824d5c682a9af86e32a0b7b2cb220caa82bd637910393a3d5b4e0c783","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-07T00:13:36Z","title_canon_sha256":"558b578fc7d61f83778c164cac1acc455e022774f80d765bc0b70ba54526b30f"},"schema_version":"1.0","source":{"id":"2601.06163","kind":"arxiv","version":2}},"canonical_sha256":"8c7b934d4a46e964bf1cf2ab58af25e286299deed52324dec85d6c9ad61a2fe2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8c7b934d4a46e964bf1cf2ab58af25e286299deed52324dec85d6c9ad61a2fe2","first_computed_at":"2026-05-20T00:04:20.836966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:20.836966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LkU0Vuf1Rqy/qRyxAlepBWLIzaDw/+36qvFJHewA3EACe7exggNcS72nOiKHdGLEVSlkFGjIoLDc1/P17yMuAQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:20.837805Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.06163","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:17556258d890c5e869627d3ebc8e112f96a0f5a72dabc27b15b76e9beea17d53","sha256:7fc2cb153ae57f098b0e423361a6ba51b1e1486bb32b3ebc40b793411d5c6135"],"state_sha256":"7793484ed7ca728af0cc702e5bacc707e09c9d80d1f5c9743d727e7dbb0a2d5c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4xtoyoVBsNMBiUL9Izv4F/8WW6V+D6VLJEMFpxCpYSbJWdmsZCVOAwZoMi4STsQFfafLoDdeo83rScs/mNsZDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T17:07:39.816598Z","bundle_sha256":"bcb3001fbc0912b1acfaae8fbf2f835aed36632c31c2716e7f08ec86e79d9355"}}