{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JHCF5H2BGYPYTQKQX74T6FC4IJ","short_pith_number":"pith:JHCF5H2B","schema_version":"1.0","canonical_sha256":"49c45e9f41361f89c150bff93f145c424524e3e0ffb8efb503a54d010d78065c","source":{"kind":"arxiv","id":"2602.02886","version":2},"attestation_state":"computed","paper":{"title":"Mixture of Concept Bottleneck Experts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arianna Casanova, Danilo Giordano, Francesco De Santis, Francesco Giannini, Gabriele Ciravegna, Giovanni De Felice, Johannes Schneider, Mateo Espinosa Zarlenga, Michelangelo Diligenti, Pietro Barbiero","submitted_at":"2026-02-02T22:44:42Z","abstract_excerpt":"Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor to a single expression whose functional form is set a priori, limiting both predictive accuracy and adaptability to diverse user needs. We propose Mixture of Concept Bottleneck Experts (M-CBE), a framework that generalizes existing CBMs along two dimensions: the number of expressions, referred to as experts, employed by the task predictor to map concepts to the task, and the functional form each expression takes, th"},"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":"2602.02886","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-02T22:44:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4fdbd8397caa5c6f92f46ca82f9e52e33ef0fc7ae526a4717760921b9b5abd3a","abstract_canon_sha256":"00d2f96a3ad590ff9b084a16d5ba807fb5daee0f6e5fb8454b1e8ef63426af81"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:02:31.781953Z","signature_b64":"luR+EFf/DZMsKjJWlAT8w94hQqG7Ub0GQP+D38nxo3P0G+/tskQovuPW+mcLp/Ae47AIuRGZ/jRUoWatORwfCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49c45e9f41361f89c150bff93f145c424524e3e0ffb8efb503a54d010d78065c","last_reissued_at":"2026-06-01T01:02:31.781034Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:02:31.781034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mixture of Concept Bottleneck Experts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Arianna Casanova, Danilo Giordano, Francesco De Santis, Francesco Giannini, Gabriele Ciravegna, Giovanni De Felice, Johannes Schneider, Mateo Espinosa Zarlenga, Michelangelo Diligenti, Pietro Barbiero","submitted_at":"2026-02-02T22:44:42Z","abstract_excerpt":"Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor to a single expression whose functional form is set a priori, limiting both predictive accuracy and adaptability to diverse user needs. We propose Mixture of Concept Bottleneck Experts (M-CBE), a framework that generalizes existing CBMs along two dimensions: the number of expressions, referred to as experts, employed by the task predictor to map concepts to the task, and the functional form each expression takes, th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.02886","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/2602.02886/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":"2602.02886","created_at":"2026-06-01T01:02:31.781189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.02886v2","created_at":"2026-06-01T01:02:31.781189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.02886","created_at":"2026-06-01T01:02:31.781189+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHCF5H2BGYPY","created_at":"2026-06-01T01:02:31.781189+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHCF5H2BGYPYTQKQ","created_at":"2026-06-01T01:02:31.781189+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHCF5H2B","created_at":"2026-06-01T01:02:31.781189+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/JHCF5H2BGYPYTQKQX74T6FC4IJ","json":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ.json","graph_json":"https://pith.science/api/pith-number/JHCF5H2BGYPYTQKQX74T6FC4IJ/graph.json","events_json":"https://pith.science/api/pith-number/JHCF5H2BGYPYTQKQX74T6FC4IJ/events.json","paper":"https://pith.science/paper/JHCF5H2B"},"agent_actions":{"view_html":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ","download_json":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ.json","view_paper":"https://pith.science/paper/JHCF5H2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.02886&json=true","fetch_graph":"https://pith.science/api/pith-number/JHCF5H2BGYPYTQKQX74T6FC4IJ/graph.json","fetch_events":"https://pith.science/api/pith-number/JHCF5H2BGYPYTQKQX74T6FC4IJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ/action/storage_attestation","attest_author":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ/action/author_attestation","sign_citation":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ/action/citation_signature","submit_replication":"https://pith.science/pith/JHCF5H2BGYPYTQKQX74T6FC4IJ/action/replication_record"}},"created_at":"2026-06-01T01:02:31.781189+00:00","updated_at":"2026-06-01T01:02:31.781189+00:00"}