{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AVGW3NEXZTR2FSRNVXMX3NZBIL","short_pith_number":"pith:AVGW3NEX","schema_version":"1.0","canonical_sha256":"054d6db497cce3a2ca2dadd97db72142d5c4ef133b1e1432ac9ba5594f28a461","source":{"kind":"arxiv","id":"2608.11287","version":1},"attestation_state":"computed","paper":{"title":"CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Gawon Lim","submitted_at":"2026-08-11T15:45:09Z","abstract_excerpt":"Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While existing methods address imbalance through re-balancing, adjustment, representation learning, or multi-expert modeling, they rarely estimate which expert should be trusted for each class. This paper proposes CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification. CLEAR generates diverse experts through threshold-based structured sampli"},"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":"2608.11287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-11T15:45:09Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0b83a8ec955871df5334ffbe5fcaaca64568fd8051ac279976e0c699afa28f48","abstract_canon_sha256":"4c787b94d81dbf5f6066a44c99090cde7a721754808ed25c2b7b9a7931d17146"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-13T00:20:58.030274Z","signature_b64":"qw5/jsYqesrMyxGc2fsZH1HQq+udGHKZ3UhjOBUGyZXu+jVbgbcT+xQEs33Oquy3YIuCeax9GUkdgaMaDnVPAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"054d6db497cce3a2ca2dadd97db72142d5c4ef133b1e1432ac9ba5594f28a461","last_reissued_at":"2026-08-13T00:20:58.028809Z","signature_status":"signed_v1","first_computed_at":"2026-08-13T00:20:58.028809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Gawon Lim","submitted_at":"2026-08-11T15:45:09Z","abstract_excerpt":"Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While existing methods address imbalance through re-balancing, adjustment, representation learning, or multi-expert modeling, they rarely estimate which expert should be trusted for each class. This paper proposes CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification. CLEAR generates diverse experts through threshold-based structured sampli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.11287","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/2608.11287/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":"2608.11287","created_at":"2026-08-13T00:20:58.029084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.11287v1","created_at":"2026-08-13T00:20:58.029084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.11287","created_at":"2026-08-13T00:20:58.029084+00:00"},{"alias_kind":"pith_short_12","alias_value":"AVGW3NEXZTR2","created_at":"2026-08-13T00:20:58.029084+00:00"},{"alias_kind":"pith_short_16","alias_value":"AVGW3NEXZTR2FSRN","created_at":"2026-08-13T00:20:58.029084+00:00"},{"alias_kind":"pith_short_8","alias_value":"AVGW3NEX","created_at":"2026-08-13T00:20:58.029084+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/AVGW3NEXZTR2FSRNVXMX3NZBIL","json":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL.json","graph_json":"https://pith.science/api/pith-number/AVGW3NEXZTR2FSRNVXMX3NZBIL/graph.json","events_json":"https://pith.science/api/pith-number/AVGW3NEXZTR2FSRNVXMX3NZBIL/events.json","paper":"https://pith.science/paper/AVGW3NEX"},"agent_actions":{"view_html":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL","download_json":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL.json","view_paper":"https://pith.science/paper/AVGW3NEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.11287&json=true","fetch_graph":"https://pith.science/api/pith-number/AVGW3NEXZTR2FSRNVXMX3NZBIL/graph.json","fetch_events":"https://pith.science/api/pith-number/AVGW3NEXZTR2FSRNVXMX3NZBIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL/action/storage_attestation","attest_author":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL/action/author_attestation","sign_citation":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL/action/citation_signature","submit_replication":"https://pith.science/pith/AVGW3NEXZTR2FSRNVXMX3NZBIL/action/replication_record"}},"created_at":"2026-08-13T00:20:58.029084+00:00","updated_at":"2026-08-13T00:20:58.029084+00:00"}