{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:T6HXDXI5T6DCCNYEMDWBKLT24O","short_pith_number":"pith:T6HXDXI5","canonical_record":{"source":{"id":"2604.15166","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-16T15:46:02Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9599830383a0f2641a0b445a59d5275738d96d4977a5d3214365f5153c26e668","abstract_canon_sha256":"c0ad3f258483854b11b7e8c6a5cfb15767e9be967efcd7e68a2d2e6a8ac57a69"},"schema_version":"1.0"},"canonical_sha256":"9f8f71dd1d9f8621370460ec152e7ae38ebf59870be78a60f56e61b6503c9373","source":{"kind":"arxiv","id":"2604.15166","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.15166","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"arxiv_version","alias_value":"2604.15166v2","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.15166","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"pith_short_12","alias_value":"T6HXDXI5T6DC","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"pith_short_16","alias_value":"T6HXDXI5T6DCCNYE","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"pith_short_8","alias_value":"T6HXDXI5","created_at":"2026-05-20T02:05:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:T6HXDXI5T6DCCNYEMDWBKLT24O","target":"record","payload":{"canonical_record":{"source":{"id":"2604.15166","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-16T15:46:02Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9599830383a0f2641a0b445a59d5275738d96d4977a5d3214365f5153c26e668","abstract_canon_sha256":"c0ad3f258483854b11b7e8c6a5cfb15767e9be967efcd7e68a2d2e6a8ac57a69"},"schema_version":"1.0"},"canonical_sha256":"9f8f71dd1d9f8621370460ec152e7ae38ebf59870be78a60f56e61b6503c9373","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T02:05:43.204758Z","signature_b64":"hOoy4w14JwQN+29joNgmkVS+HyUbj/kgKF/5ITuVFLIqOwfYD/DbetMgs1SzUzF2vot0sWFf2dboFJNiTsTADg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f8f71dd1d9f8621370460ec152e7ae38ebf59870be78a60f56e61b6503c9373","last_reissued_at":"2026-05-20T02:05:43.203711Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T02:05:43.203711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.15166","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":"2026-05-20T02:05:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a6miE6Ijr3emtJqQZbLXAqPRqPz8aMcvNl6tcFdRySGzWEP+BRTTYZY6CtVbi8jcAhBlzjuMhhfjImLf6p7dBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:33:58.269300Z"},"content_sha256":"bb7ef095050753823745f2fbf0fe0f9af4ede3bf63bd3fcab58248041748a389","schema_version":"1.0","event_id":"sha256:bb7ef095050753823745f2fbf0fe0f9af4ede3bf63bd3fcab58248041748a389"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:T6HXDXI5T6DCCNYEMDWBKLT24O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Class Unlearning via Depth-Aware Removal of Forget-Specific Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"By projecting out forget-specific directions layer by layer with depth-aware scaling, a closed-form method achieves class unlearning closer to full retraining than prior approaches.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Arman Hatami, Ilya E. Monosov, Romina Aalishah","submitted_at":"2026-04-16T15:46:02Z","abstract_excerpt":"Machine unlearning aims to remove targeted knowledge from a trained model without the cost of retraining from scratch. In class unlearning, however, reducing accuracy on forget classes does not necessarily imply true forgetting: forgotten information can remain encoded in internal representations, and apparent forgetting may arise from classifier-head suppression rather than representational removal. We show that existing class-unlearning methods often exhibit weak or negative selectivity, preserve forget-class structure in deep representations, or rely heavily on final-layer bias shifts. We t"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Across MNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet, and across convolutional and transformer architectures, DAMP more closely resembles the retraining gold standard than some of the prior methods, improving selective forgetting while better preserving retain-class performance and reducing residual forget-class structure in deep layers.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That forget directions extracted as residuals relative to retain-class prototypes at each layer accurately isolate the targeted knowledge, and that the parameter-free depth-aware scaling derived from probe separability will preserve utility without introducing new failure modes on retain classes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DAMP performs one-shot class unlearning by extracting and projecting out forget-specific residual directions at each network depth using class prototypes and a separability-derived scaling rule.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"By projecting out forget-specific directions layer by layer with depth-aware scaling, a closed-form method achieves class unlearning closer to full retraining than prior approaches.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b40c76d111c9bac4a0b4b8059e503eca22e0e488a746da4a466244dc9022aee2"},"source":{"id":"2604.15166","kind":"arxiv","version":2},"verdict":{"id":"ce474362-0a7f-4108-bf0c-ce88d88df969","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T11:42:48.804913Z","strongest_claim":"Across MNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet, and across convolutional and transformer architectures, DAMP more closely resembles the retraining gold standard than some of the prior methods, improving selective forgetting while better preserving retain-class performance and reducing residual forget-class structure in deep layers.","one_line_summary":"DAMP performs one-shot class unlearning by extracting and projecting out forget-specific residual directions at each network depth using class prototypes and a separability-derived scaling rule.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That forget directions extracted as residuals relative to retain-class prototypes at each layer accurately isolate the targeted knowledge, and that the parameter-free depth-aware scaling derived from probe separability will preserve utility without introducing new failure modes on retain classes.","pith_extraction_headline":"By projecting out forget-specific directions layer by layer with depth-aware scaling, a closed-form method achieves class unlearning closer to full retraining than prior approaches."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.15166/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":"ce474362-0a7f-4108-bf0c-ce88d88df969"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T02:05:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MLWdeq07njBMWHxjfC8HKKZPN6qbCK/jK1NvIDd0qp9vMb7hY9CiVcgz4MghxRvYrmFwClcXEgvVItGzkPbbBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:33:58.270251Z"},"content_sha256":"1646eb596f844654c1ebfbb2383e71cbc631926a765397f7644535b8b853460a","schema_version":"1.0","event_id":"sha256:1646eb596f844654c1ebfbb2383e71cbc631926a765397f7644535b8b853460a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T6HXDXI5T6DCCNYEMDWBKLT24O/bundle.json","state_url":"https://pith.science/pith/T6HXDXI5T6DCCNYEMDWBKLT24O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T6HXDXI5T6DCCNYEMDWBKLT24O/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":"2026-05-28T18:33:58Z","links":{"resolver":"https://pith.science/pith/T6HXDXI5T6DCCNYEMDWBKLT24O","bundle":"https://pith.science/pith/T6HXDXI5T6DCCNYEMDWBKLT24O/bundle.json","state":"https://pith.science/pith/T6HXDXI5T6DCCNYEMDWBKLT24O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T6HXDXI5T6DCCNYEMDWBKLT24O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:T6HXDXI5T6DCCNYEMDWBKLT24O","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":"c0ad3f258483854b11b7e8c6a5cfb15767e9be967efcd7e68a2d2e6a8ac57a69","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-16T15:46:02Z","title_canon_sha256":"9599830383a0f2641a0b445a59d5275738d96d4977a5d3214365f5153c26e668"},"schema_version":"1.0","source":{"id":"2604.15166","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.15166","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"arxiv_version","alias_value":"2604.15166v2","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.15166","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"pith_short_12","alias_value":"T6HXDXI5T6DC","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"pith_short_16","alias_value":"T6HXDXI5T6DCCNYE","created_at":"2026-05-20T02:05:43Z"},{"alias_kind":"pith_short_8","alias_value":"T6HXDXI5","created_at":"2026-05-20T02:05:43Z"}],"graph_snapshots":[{"event_id":"sha256:1646eb596f844654c1ebfbb2383e71cbc631926a765397f7644535b8b853460a","target":"graph","created_at":"2026-05-20T02:05:43Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Across MNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet, and across convolutional and transformer architectures, DAMP more closely resembles the retraining gold standard than some of the prior methods, improving selective forgetting while better preserving retain-class performance and reducing residual forget-class structure in deep layers."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That forget directions extracted as residuals relative to retain-class prototypes at each layer accurately isolate the targeted knowledge, and that the parameter-free depth-aware scaling derived from probe separability will preserve utility without introducing new failure modes on retain classes."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DAMP performs one-shot class unlearning by extracting and projecting out forget-specific residual directions at each network depth using class prototypes and a separability-derived scaling rule."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"By projecting out forget-specific directions layer by layer with depth-aware scaling, a closed-form method achieves class unlearning closer to full retraining than prior approaches."}],"snapshot_sha256":"b40c76d111c9bac4a0b4b8059e503eca22e0e488a746da4a466244dc9022aee2"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.15166/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine unlearning aims to remove targeted knowledge from a trained model without the cost of retraining from scratch. In class unlearning, however, reducing accuracy on forget classes does not necessarily imply true forgetting: forgotten information can remain encoded in internal representations, and apparent forgetting may arise from classifier-head suppression rather than representational removal. We show that existing class-unlearning methods often exhibit weak or negative selectivity, preserve forget-class structure in deep representations, or rely heavily on final-layer bias shifts. We t","authors_text":"Arman Hatami, Ilya E. Monosov, Romina Aalishah","cross_cats":["cs.AI","cs.LG"],"headline":"By projecting out forget-specific directions layer by layer with depth-aware scaling, a closed-form method achieves class unlearning closer to full retraining than prior approaches.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-16T15:46:02Z","title":"Class Unlearning via Depth-Aware Removal of Forget-Specific Directions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.15166","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T11:42:48.804913Z","id":"ce474362-0a7f-4108-bf0c-ce88d88df969","model_set":{"reader":"grok-4.3"},"one_line_summary":"DAMP performs one-shot class unlearning by extracting and projecting out forget-specific residual directions at each network depth using class prototypes and a separability-derived scaling rule.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"By projecting out forget-specific directions layer by layer with depth-aware scaling, a closed-form method achieves class unlearning closer to full retraining than prior approaches.","strongest_claim":"Across MNIST, CIFAR-10, CIFAR-100, and Tiny ImageNet, and across convolutional and transformer architectures, DAMP more closely resembles the retraining gold standard than some of the prior methods, improving selective forgetting while better preserving retain-class performance and reducing residual forget-class structure in deep layers.","weakest_assumption":"That forget directions extracted as residuals relative to retain-class prototypes at each layer accurately isolate the targeted knowledge, and that the parameter-free depth-aware scaling derived from probe separability will preserve utility without introducing new failure modes on retain classes."}},"verdict_id":"ce474362-0a7f-4108-bf0c-ce88d88df969"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bb7ef095050753823745f2fbf0fe0f9af4ede3bf63bd3fcab58248041748a389","target":"record","created_at":"2026-05-20T02:05:43Z","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":"c0ad3f258483854b11b7e8c6a5cfb15767e9be967efcd7e68a2d2e6a8ac57a69","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-16T15:46:02Z","title_canon_sha256":"9599830383a0f2641a0b445a59d5275738d96d4977a5d3214365f5153c26e668"},"schema_version":"1.0","source":{"id":"2604.15166","kind":"arxiv","version":2}},"canonical_sha256":"9f8f71dd1d9f8621370460ec152e7ae38ebf59870be78a60f56e61b6503c9373","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f8f71dd1d9f8621370460ec152e7ae38ebf59870be78a60f56e61b6503c9373","first_computed_at":"2026-05-20T02:05:43.203711Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T02:05:43.203711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hOoy4w14JwQN+29joNgmkVS+HyUbj/kgKF/5ITuVFLIqOwfYD/DbetMgs1SzUzF2vot0sWFf2dboFJNiTsTADg==","signature_status":"signed_v1","signed_at":"2026-05-20T02:05:43.204758Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.15166","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb7ef095050753823745f2fbf0fe0f9af4ede3bf63bd3fcab58248041748a389","sha256:1646eb596f844654c1ebfbb2383e71cbc631926a765397f7644535b8b853460a"],"state_sha256":"380f958e5e73ffb7a2592d5e58958ae22433210b106747b86ecd22986e533091"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SkVrSisysW54cO9Kdtes6eBiwrN5FisvQl+3CTjlalYJKv9WC41sJlWaMNCfqP6FK3hY3STtvBHGeRKnvTicAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T18:33:58.274866Z","bundle_sha256":"45065b6a1979f748c02295fa7561989c1a76f558fda6fcc1bfabc7296b3d0b1b"}}