{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:I74DZCF3DDNTZJ3JIY467L2I7O","short_pith_number":"pith:I74DZCF3","schema_version":"1.0","canonical_sha256":"47f83c88bb18db3ca7694639efaf48fb961c023fbecd52bc772f1bc32934924b","source":{"kind":"arxiv","id":"2205.15764","version":3},"attestation_state":"computed","paper":{"title":"SymFormer: End-to-end symbolic regression using transformer-based architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Erik Derner, Ji\\v{r}\\'i Kubal\\'ik, Jon\\'a\\v{s} Kulh\\'anek, Martin Vastl, Robert Babu\\v{s}ka","submitted_at":"2022-05-31T13:01:50Z","abstract_excerpt":"Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied to symbolic regression, among which the transformer-based ones seem to be the most promising. After training the transformer on a large number of formulas (in the order of days), the actual inference, i.e., finding a formula for new, unseen data, is very fast (in the order of seconds). This is considerably faster than state-of-the-art evolutionary methods. The main drawback"},"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":"2205.15764","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-31T13:01:50Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"2b33a68ecc152edd725f90b476faaecf939199598a5a88e3a5c210b9fc56469e","abstract_canon_sha256":"3ecaf1f004376592b557b5bed2028858d0f0d9eabe8734280016132ab512372f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:08:52.269473Z","signature_b64":"W5ZqqQ8zQb/tQl3aENDuwoL5g+6/0JT9y4lw2Vcj24Ju94iuJplZMDrOAmOca9psTxTpoeTZDTmUvlRtjnNGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47f83c88bb18db3ca7694639efaf48fb961c023fbecd52bc772f1bc32934924b","last_reissued_at":"2026-07-05T05:08:52.269062Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:08:52.269062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SymFormer: End-to-end symbolic regression using transformer-based architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.LG","authors_text":"Erik Derner, Ji\\v{r}\\'i Kubal\\'ik, Jon\\'a\\v{s} Kulh\\'anek, Martin Vastl, Robert Babu\\v{s}ka","submitted_at":"2022-05-31T13:01:50Z","abstract_excerpt":"Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied to symbolic regression, among which the transformer-based ones seem to be the most promising. After training the transformer on a large number of formulas (in the order of days), the actual inference, i.e., finding a formula for new, unseen data, is very fast (in the order of seconds). This is considerably faster than state-of-the-art evolutionary methods. The main drawback"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15764","kind":"arxiv","version":3},"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/2205.15764/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":"2205.15764","created_at":"2026-07-05T05:08:52.269116+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.15764v3","created_at":"2026-07-05T05:08:52.269116+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15764","created_at":"2026-07-05T05:08:52.269116+00:00"},{"alias_kind":"pith_short_12","alias_value":"I74DZCF3DDNT","created_at":"2026-07-05T05:08:52.269116+00:00"},{"alias_kind":"pith_short_16","alias_value":"I74DZCF3DDNTZJ3J","created_at":"2026-07-05T05:08:52.269116+00:00"},{"alias_kind":"pith_short_8","alias_value":"I74DZCF3","created_at":"2026-07-05T05:08:52.269116+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2502.01476","citing_title":"Neuro-Symbolic AI for Analytical Solutions of Differential Equations","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O","json":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O.json","graph_json":"https://pith.science/api/pith-number/I74DZCF3DDNTZJ3JIY467L2I7O/graph.json","events_json":"https://pith.science/api/pith-number/I74DZCF3DDNTZJ3JIY467L2I7O/events.json","paper":"https://pith.science/paper/I74DZCF3"},"agent_actions":{"view_html":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O","download_json":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O.json","view_paper":"https://pith.science/paper/I74DZCF3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.15764&json=true","fetch_graph":"https://pith.science/api/pith-number/I74DZCF3DDNTZJ3JIY467L2I7O/graph.json","fetch_events":"https://pith.science/api/pith-number/I74DZCF3DDNTZJ3JIY467L2I7O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O/action/storage_attestation","attest_author":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O/action/author_attestation","sign_citation":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O/action/citation_signature","submit_replication":"https://pith.science/pith/I74DZCF3DDNTZJ3JIY467L2I7O/action/replication_record"}},"created_at":"2026-07-05T05:08:52.269116+00:00","updated_at":"2026-07-05T05:08:52.269116+00:00"}