{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:U2U2RXA5DTY7DPBVPJGX3YQELS","short_pith_number":"pith:U2U2RXA5","schema_version":"1.0","canonical_sha256":"a6a9a8dc1d1cf1f1bc357a4d7de2045c8e86870c76cac00efe3ef5a2e63744a6","source":{"kind":"arxiv","id":"2606.05695","version":1},"attestation_state":"computed","paper":{"title":"Revisiting Prototype Rehearsal for Exemplar-Free Continual Learning: Manifold-Aware Boundary Sampling with Adaptive Class-Balanced Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bartosz Krawczyk, Hongye Xu","submitted_at":"2026-06-04T04:27:01Z","abstract_excerpt":"Exemplar-free class-incremental learning (EFCIL) aims to acquire new classes over time without storing raw data. Historically, prototype rehearsal, which samples around stored class prototypes and mixes them with current-task data, has been a popular strategy to reduce catastrophic forgetting. However, recent drift-compensation methods that explicitly realign prototypes in the evolving feature space consistently outperform prototype-based rehearsal, raising the question of whether rehearsal itself is fundamentally limited. We argue that the performance gap stems not from the idea of prototype "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.05695","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-04T04:27:01Z","cross_cats_sorted":[],"title_canon_sha256":"9ac22a35f76dedf0782368898897d03c49f52b949aa6d5df194641681f718161","abstract_canon_sha256":"bd82e64b8ef7b460d2e5cf575517c83a1ad52d45a82134ce69955751103618d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:14:59.533715Z","signature_b64":"wQRFkqYw0epAiatEmrdkJn+xE6V3XQLru8kb26XGCdttrLBswqt87WhhzBXo6xALjyE0Hax+Lfr0lOo6TOxzDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6a9a8dc1d1cf1f1bc357a4d7de2045c8e86870c76cac00efe3ef5a2e63744a6","last_reissued_at":"2026-06-05T01:14:59.533290Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:14:59.533290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Prototype Rehearsal for Exemplar-Free Continual Learning: Manifold-Aware Boundary Sampling with Adaptive Class-Balanced Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bartosz Krawczyk, Hongye Xu","submitted_at":"2026-06-04T04:27:01Z","abstract_excerpt":"Exemplar-free class-incremental learning (EFCIL) aims to acquire new classes over time without storing raw data. Historically, prototype rehearsal, which samples around stored class prototypes and mixes them with current-task data, has been a popular strategy to reduce catastrophic forgetting. However, recent drift-compensation methods that explicitly realign prototypes in the evolving feature space consistently outperform prototype-based rehearsal, raising the question of whether rehearsal itself is fundamentally limited. We argue that the performance gap stems not from the idea of prototype "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05695","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/2606.05695/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.05695","created_at":"2026-06-05T01:14:59.533358+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05695v1","created_at":"2026-06-05T01:14:59.533358+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05695","created_at":"2026-06-05T01:14:59.533358+00:00"},{"alias_kind":"pith_short_12","alias_value":"U2U2RXA5DTY7","created_at":"2026-06-05T01:14:59.533358+00:00"},{"alias_kind":"pith_short_16","alias_value":"U2U2RXA5DTY7DPBV","created_at":"2026-06-05T01:14:59.533358+00:00"},{"alias_kind":"pith_short_8","alias_value":"U2U2RXA5","created_at":"2026-06-05T01:14:59.533358+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS","json":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS.json","graph_json":"https://pith.science/api/pith-number/U2U2RXA5DTY7DPBVPJGX3YQELS/graph.json","events_json":"https://pith.science/api/pith-number/U2U2RXA5DTY7DPBVPJGX3YQELS/events.json","paper":"https://pith.science/paper/U2U2RXA5"},"agent_actions":{"view_html":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS","download_json":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS.json","view_paper":"https://pith.science/paper/U2U2RXA5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05695&json=true","fetch_graph":"https://pith.science/api/pith-number/U2U2RXA5DTY7DPBVPJGX3YQELS/graph.json","fetch_events":"https://pith.science/api/pith-number/U2U2RXA5DTY7DPBVPJGX3YQELS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS/action/storage_attestation","attest_author":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS/action/author_attestation","sign_citation":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS/action/citation_signature","submit_replication":"https://pith.science/pith/U2U2RXA5DTY7DPBVPJGX3YQELS/action/replication_record"}},"created_at":"2026-06-05T01:14:59.533358+00:00","updated_at":"2026-06-05T01:14:59.533358+00:00"}