{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZWQ6CLDW4MTD77ZSWEQPSJCJVO","short_pith_number":"pith:ZWQ6CLDW","schema_version":"1.0","canonical_sha256":"cda1e12c76e3263fff32b120f92449ab8a0aa757ff6a06544658f31703c215ca","source":{"kind":"arxiv","id":"2312.01816","version":2},"attestation_state":"computed","paper":{"title":"Class Symbolic Regression: Gotta Fit 'Em All","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA","astro-ph.IM","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Foivos I. Diakogiannis, Rodrigo Ibata, Thibaut L. Fran\\c{c}ois, Wassim Tenachi","submitted_at":"2023-12-04T11:45:44Z","abstract_excerpt":"We introduce 'Class Symbolic Regression' (Class SR) a first framework for automatically finding a single analytical functional form that accurately fits multiple datasets - each realization being governed by its own (possibly) unique set of fitting parameters. This hierarchical framework leverages the common constraint that all the members of a single class of physical phenomena follow a common governing law. Our approach extends the capabilities of our earlier Physical Symbolic Optimization ($\\Phi$-SO) framework for Symbolic Regression, which integrates dimensional analysis constraints and de"},"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":"2312.01816","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-04T11:45:44Z","cross_cats_sorted":["astro-ph.GA","astro-ph.IM","physics.comp-ph"],"title_canon_sha256":"049ba6bb6ce6d6f6b5030c67de5470498f33aef161bf58d8c28bac455a152d19","abstract_canon_sha256":"03b5edf154ba659a5cca0b5ceb794be006028d66242879d8edc1b864986c191f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:59.952734Z","signature_b64":"ujr+2ESauIIzLSsQLlNEj1wq8ZZGlni0joXQ6t6eBtKsEcU2vNLu6mpSaaWnB49+iznnmAO7rMBgY6Q5z58TCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cda1e12c76e3263fff32b120f92449ab8a0aa757ff6a06544658f31703c215ca","last_reissued_at":"2026-07-05T08:32:59.952195Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:59.952195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Class Symbolic Regression: Gotta Fit 'Em All","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.GA","astro-ph.IM","physics.comp-ph"],"primary_cat":"cs.LG","authors_text":"Foivos I. Diakogiannis, Rodrigo Ibata, Thibaut L. Fran\\c{c}ois, Wassim Tenachi","submitted_at":"2023-12-04T11:45:44Z","abstract_excerpt":"We introduce 'Class Symbolic Regression' (Class SR) a first framework for automatically finding a single analytical functional form that accurately fits multiple datasets - each realization being governed by its own (possibly) unique set of fitting parameters. This hierarchical framework leverages the common constraint that all the members of a single class of physical phenomena follow a common governing law. Our approach extends the capabilities of our earlier Physical Symbolic Optimization ($\\Phi$-SO) framework for Symbolic Regression, which integrates dimensional analysis constraints and de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01816","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/2312.01816/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":"2312.01816","created_at":"2026-07-05T08:32:59.952250+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.01816v2","created_at":"2026-07-05T08:32:59.952250+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01816","created_at":"2026-07-05T08:32:59.952250+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZWQ6CLDW4MTD","created_at":"2026-07-05T08:32:59.952250+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZWQ6CLDW4MTD77ZS","created_at":"2026-07-05T08:32:59.952250+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZWQ6CLDW","created_at":"2026-07-05T08:32:59.952250+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/ZWQ6CLDW4MTD77ZSWEQPSJCJVO","json":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO.json","graph_json":"https://pith.science/api/pith-number/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/graph.json","events_json":"https://pith.science/api/pith-number/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/events.json","paper":"https://pith.science/paper/ZWQ6CLDW"},"agent_actions":{"view_html":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO","download_json":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO.json","view_paper":"https://pith.science/paper/ZWQ6CLDW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.01816&json=true","fetch_graph":"https://pith.science/api/pith-number/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/action/storage_attestation","attest_author":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/action/author_attestation","sign_citation":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/action/citation_signature","submit_replication":"https://pith.science/pith/ZWQ6CLDW4MTD77ZSWEQPSJCJVO/action/replication_record"}},"created_at":"2026-07-05T08:32:59.952250+00:00","updated_at":"2026-07-05T08:32:59.952250+00:00"}