{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L5C6M6YWOEGSCJKBH4VFEN2U73","short_pith_number":"pith:L5C6M6YW","schema_version":"1.0","canonical_sha256":"5f45e67b16710d2125413f2a523754fed544f9a9690ba771636b3c88b03e86db","source":{"kind":"arxiv","id":"2504.15240","version":1},"attestation_state":"computed","paper":{"title":"Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexander Heinlein, Amanda A. Howard, Amirhossein Mollaali, Christian Bolivar Moya, Guang Lin, Panos Stinis","submitted_at":"2025-04-21T17:14:05Z","abstract_excerpt":"This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov-Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. Building on this, we introduce Conformalized-KANs, which integrate conformal prediction, a distribution-free UQ technique, with KAN ensembles to generate calibrated prediction intervals with guaranteed coverage. Extensive numerical experiments are conducted to evaluate the effectiveness of these methods, focusing particularly on the robu"},"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":"2504.15240","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-21T17:14:05Z","cross_cats_sorted":[],"title_canon_sha256":"062319f3a5ba6ce3d53340694dd88db5348d87cd5d1c5870406e39c0bde1c3ca","abstract_canon_sha256":"c5cccb671511ade55d42e842f5ee24ce1f870fb37e390f0ff4438d587059d6fe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:58.658802Z","signature_b64":"ajBYatGQj4X3gd1S3/FCYgCYOgoSdLbFnfGrjvOVPHEg69I38C0Ytqe2q7jKEevLBEpsGr4+FNnQ9OqV35/rAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f45e67b16710d2125413f2a523754fed544f9a9690ba771636b3c88b03e86db","last_reissued_at":"2026-07-05T10:51:58.658398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:58.658398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexander Heinlein, Amanda A. Howard, Amirhossein Mollaali, Christian Bolivar Moya, Guang Lin, Panos Stinis","submitted_at":"2025-04-21T17:14:05Z","abstract_excerpt":"This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov-Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure of UQ, enhancing interpretability and robustness in modeling complex functions. Building on this, we introduce Conformalized-KANs, which integrate conformal prediction, a distribution-free UQ technique, with KAN ensembles to generate calibrated prediction intervals with guaranteed coverage. Extensive numerical experiments are conducted to evaluate the effectiveness of these methods, focusing particularly on the robu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.15240","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/2504.15240/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":"2504.15240","created_at":"2026-07-05T10:51:58.658454+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.15240v1","created_at":"2026-07-05T10:51:58.658454+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.15240","created_at":"2026-07-05T10:51:58.658454+00:00"},{"alias_kind":"pith_short_12","alias_value":"L5C6M6YWOEGS","created_at":"2026-07-05T10:51:58.658454+00:00"},{"alias_kind":"pith_short_16","alias_value":"L5C6M6YWOEGSCJKB","created_at":"2026-07-05T10:51:58.658454+00:00"},{"alias_kind":"pith_short_8","alias_value":"L5C6M6YW","created_at":"2026-07-05T10:51:58.658454+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03302","citing_title":"Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73","json":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73.json","graph_json":"https://pith.science/api/pith-number/L5C6M6YWOEGSCJKBH4VFEN2U73/graph.json","events_json":"https://pith.science/api/pith-number/L5C6M6YWOEGSCJKBH4VFEN2U73/events.json","paper":"https://pith.science/paper/L5C6M6YW"},"agent_actions":{"view_html":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73","download_json":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73.json","view_paper":"https://pith.science/paper/L5C6M6YW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.15240&json=true","fetch_graph":"https://pith.science/api/pith-number/L5C6M6YWOEGSCJKBH4VFEN2U73/graph.json","fetch_events":"https://pith.science/api/pith-number/L5C6M6YWOEGSCJKBH4VFEN2U73/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73/action/storage_attestation","attest_author":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73/action/author_attestation","sign_citation":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73/action/citation_signature","submit_replication":"https://pith.science/pith/L5C6M6YWOEGSCJKBH4VFEN2U73/action/replication_record"}},"created_at":"2026-07-05T10:51:58.658454+00:00","updated_at":"2026-07-05T10:51:58.658454+00:00"}