{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WIJJCJG2NW7IXDOCDFRDQC6LC4","short_pith_number":"pith:WIJJCJG2","schema_version":"1.0","canonical_sha256":"b2129124da6dbe8b8dc21962380bcb172d9e7199de35cbaf6761ff4ed1c1dc7c","source":{"kind":"arxiv","id":"2412.10473","version":1},"attestation_state":"computed","paper":{"title":"CONCLAD: COntinuous Novel CLAss Detector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amanda Rios, Ibrahima Ndiour, Nilesh Ahuja, Omesh Tickoo, Parual Datta","submitted_at":"2024-12-13T01:41:28Z","abstract_excerpt":"In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD (\"COntinuous Novel CLAss Detector\"), a comprehensive solution to the under-explored problem of continual novel class detection in post-deployment data. At each new task, our approach employs an iterative uncertainty estimation algorithm to differentiate between known and novel class(es) samples, and to further discriminate between the different novel classes themselves. Samples predicted to be from a novel class with high-confidence are automat"},"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":"2412.10473","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-12-13T01:41:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2b3589ed43d87b05036754303ed8ba216cc6fad8a6481c6e8d6942ca374c49e4","abstract_canon_sha256":"9adc1787fe505f9e9ebaf7df2e44b8a3c77c6e304cd251ab66c756e628debcce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:16.541194Z","signature_b64":"13BdoKUkef95mJo+45i9z+hMC0Znels9IDZ19/LRzlLR/0XkXk9vQqgMVsxBhQZYiisyHRqwBbckZz8rA+8ZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2129124da6dbe8b8dc21962380bcb172d9e7199de35cbaf6761ff4ed1c1dc7c","last_reissued_at":"2026-07-05T09:49:16.540597Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:16.540597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CONCLAD: COntinuous Novel CLAss Detector","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Amanda Rios, Ibrahima Ndiour, Nilesh Ahuja, Omesh Tickoo, Parual Datta","submitted_at":"2024-12-13T01:41:28Z","abstract_excerpt":"In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD (\"COntinuous Novel CLAss Detector\"), a comprehensive solution to the under-explored problem of continual novel class detection in post-deployment data. At each new task, our approach employs an iterative uncertainty estimation algorithm to differentiate between known and novel class(es) samples, and to further discriminate between the different novel classes themselves. Samples predicted to be from a novel class with high-confidence are automat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10473","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/2412.10473/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":"2412.10473","created_at":"2026-07-05T09:49:16.540660+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.10473v1","created_at":"2026-07-05T09:49:16.540660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10473","created_at":"2026-07-05T09:49:16.540660+00:00"},{"alias_kind":"pith_short_12","alias_value":"WIJJCJG2NW7I","created_at":"2026-07-05T09:49:16.540660+00:00"},{"alias_kind":"pith_short_16","alias_value":"WIJJCJG2NW7IXDOC","created_at":"2026-07-05T09:49:16.540660+00:00"},{"alias_kind":"pith_short_8","alias_value":"WIJJCJG2","created_at":"2026-07-05T09:49:16.540660+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/WIJJCJG2NW7IXDOCDFRDQC6LC4","json":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4.json","graph_json":"https://pith.science/api/pith-number/WIJJCJG2NW7IXDOCDFRDQC6LC4/graph.json","events_json":"https://pith.science/api/pith-number/WIJJCJG2NW7IXDOCDFRDQC6LC4/events.json","paper":"https://pith.science/paper/WIJJCJG2"},"agent_actions":{"view_html":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4","download_json":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4.json","view_paper":"https://pith.science/paper/WIJJCJG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.10473&json=true","fetch_graph":"https://pith.science/api/pith-number/WIJJCJG2NW7IXDOCDFRDQC6LC4/graph.json","fetch_events":"https://pith.science/api/pith-number/WIJJCJG2NW7IXDOCDFRDQC6LC4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4/action/storage_attestation","attest_author":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4/action/author_attestation","sign_citation":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4/action/citation_signature","submit_replication":"https://pith.science/pith/WIJJCJG2NW7IXDOCDFRDQC6LC4/action/replication_record"}},"created_at":"2026-07-05T09:49:16.540660+00:00","updated_at":"2026-07-05T09:49:16.540660+00:00"}