{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BOCMVVJH7NMBOR6UVDLNGTA4XD","short_pith_number":"pith:BOCMVVJH","schema_version":"1.0","canonical_sha256":"0b84cad527fb581747d4a8d6d34c1cb8c625d86de45530dd3db3ddc5159b2755","source":{"kind":"arxiv","id":"2606.06567","version":1},"attestation_state":"computed","paper":{"title":"Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fabricio Olivetti de Franca, Julia Reuter","submitted_at":"2026-06-04T17:29:56Z","abstract_excerpt":"Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the field, a lack of support for uncertainty quantification (UQ) limits its adoption in real-world decision processes. In regression analysis, UQ provides important information about the model reliability, which can both help to avoid overfitting by accounting for uncertainty in the data, and provide insights for decision-making. This survey is the first to clearly address "},"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":"2606.06567","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-04T17:29:56Z","cross_cats_sorted":[],"title_canon_sha256":"76796d12048edc6a5d0b78ba0ee648d03266fde8100b2988c0e6807928f991d4","abstract_canon_sha256":"23dee9180b4d7fe1cfbbb197700fe551fa7ac1aa384cd9acf7d97a722d3e8679"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T00:03:44.816847Z","signature_b64":"/4UB8aBvRaeQfJv5p+WP821GIjm0IXGUclIf849q1U93gRVCHcTx8kVd570v/OeHUlSmw2Iv7g7LyoND6/mRAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b84cad527fb581747d4a8d6d34c1cb8c625d86de45530dd3db3ddc5159b2755","last_reissued_at":"2026-06-08T00:03:44.815959Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T00:03:44.815959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fabricio Olivetti de Franca, Julia Reuter","submitted_at":"2026-06-04T17:29:56Z","abstract_excerpt":"Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the field, a lack of support for uncertainty quantification (UQ) limits its adoption in real-world decision processes. In regression analysis, UQ provides important information about the model reliability, which can both help to avoid overfitting by accounting for uncertainty in the data, and provide insights for decision-making. This survey is the first to clearly address "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06567","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/2606.06567/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":"2606.06567","created_at":"2026-06-08T00:03:44.816115+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06567v1","created_at":"2026-06-08T00:03:44.816115+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06567","created_at":"2026-06-08T00:03:44.816115+00:00"},{"alias_kind":"pith_short_12","alias_value":"BOCMVVJH7NMB","created_at":"2026-06-08T00:03:44.816115+00:00"},{"alias_kind":"pith_short_16","alias_value":"BOCMVVJH7NMBOR6U","created_at":"2026-06-08T00:03:44.816115+00:00"},{"alias_kind":"pith_short_8","alias_value":"BOCMVVJH","created_at":"2026-06-08T00:03:44.816115+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/BOCMVVJH7NMBOR6UVDLNGTA4XD","json":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD.json","graph_json":"https://pith.science/api/pith-number/BOCMVVJH7NMBOR6UVDLNGTA4XD/graph.json","events_json":"https://pith.science/api/pith-number/BOCMVVJH7NMBOR6UVDLNGTA4XD/events.json","paper":"https://pith.science/paper/BOCMVVJH"},"agent_actions":{"view_html":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD","download_json":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD.json","view_paper":"https://pith.science/paper/BOCMVVJH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06567&json=true","fetch_graph":"https://pith.science/api/pith-number/BOCMVVJH7NMBOR6UVDLNGTA4XD/graph.json","fetch_events":"https://pith.science/api/pith-number/BOCMVVJH7NMBOR6UVDLNGTA4XD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD/action/storage_attestation","attest_author":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD/action/author_attestation","sign_citation":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD/action/citation_signature","submit_replication":"https://pith.science/pith/BOCMVVJH7NMBOR6UVDLNGTA4XD/action/replication_record"}},"created_at":"2026-06-08T00:03:44.816115+00:00","updated_at":"2026-06-08T00:03:44.816115+00:00"}