{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:37SBL3WNVTFH6XDQXC3WMPPNS7","short_pith_number":"pith:37SBL3WN","schema_version":"1.0","canonical_sha256":"dfe415eecdacca7f5c70b8b7663ded97cf393533ef62336603a48b8fe6eb638a","source":{"kind":"arxiv","id":"2607.03130","version":1},"attestation_state":"computed","paper":{"title":"Copper: Unifying Correctness and Performance Specification in Code Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Andr\\'e Lizardo, Raul Barbosa","submitted_at":"2026-07-03T09:19:34Z","abstract_excerpt":"Generative AI has made remarkable progress in producing functionally correct code, yet ensuring both correctness and performance remains an open challenge. We present Copper, a framework that combines formal verification with performance-aware specification to generate code that is provably correct and efficiently executable. Our approach integrates AI-driven code synthesis with formal verification tools, and automated performance profiling loops. Evaluated on a diverse set of algorithmic and real-world programming tasks, Copper produces solutions that satisfy strict correctness guarantees whi"},"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":"2607.03130","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SE","submitted_at":"2026-07-03T09:19:34Z","cross_cats_sorted":[],"title_canon_sha256":"ab1efa0c353b19482877b600c7f96900e80612f65d01753b8d11b3a01402e233","abstract_canon_sha256":"c5837b4a974abf7989397209de23779e5b5cdb3df03301d35abb639c880824c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T01:16:44.036352Z","signature_b64":"qrc5NMVGFGl+JzcgI8H/dkhkoSducR6DQ0roO4W13yKP9mld39SpjvQl/G4QvnTFZzFk7ImO0O7zbl9tmtFvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dfe415eecdacca7f5c70b8b7663ded97cf393533ef62336603a48b8fe6eb638a","last_reissued_at":"2026-07-07T01:16:44.035921Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T01:16:44.035921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Copper: Unifying Correctness and Performance Specification in Code Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Andr\\'e Lizardo, Raul Barbosa","submitted_at":"2026-07-03T09:19:34Z","abstract_excerpt":"Generative AI has made remarkable progress in producing functionally correct code, yet ensuring both correctness and performance remains an open challenge. We present Copper, a framework that combines formal verification with performance-aware specification to generate code that is provably correct and efficiently executable. Our approach integrates AI-driven code synthesis with formal verification tools, and automated performance profiling loops. Evaluated on a diverse set of algorithmic and real-world programming tasks, Copper produces solutions that satisfy strict correctness guarantees whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.03130","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/2607.03130/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":"2607.03130","created_at":"2026-07-07T01:16:44.035981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.03130v1","created_at":"2026-07-07T01:16:44.035981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.03130","created_at":"2026-07-07T01:16:44.035981+00:00"},{"alias_kind":"pith_short_12","alias_value":"37SBL3WNVTFH","created_at":"2026-07-07T01:16:44.035981+00:00"},{"alias_kind":"pith_short_16","alias_value":"37SBL3WNVTFH6XDQ","created_at":"2026-07-07T01:16:44.035981+00:00"},{"alias_kind":"pith_short_8","alias_value":"37SBL3WN","created_at":"2026-07-07T01:16:44.035981+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/37SBL3WNVTFH6XDQXC3WMPPNS7","json":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7.json","graph_json":"https://pith.science/api/pith-number/37SBL3WNVTFH6XDQXC3WMPPNS7/graph.json","events_json":"https://pith.science/api/pith-number/37SBL3WNVTFH6XDQXC3WMPPNS7/events.json","paper":"https://pith.science/paper/37SBL3WN"},"agent_actions":{"view_html":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7","download_json":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7.json","view_paper":"https://pith.science/paper/37SBL3WN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.03130&json=true","fetch_graph":"https://pith.science/api/pith-number/37SBL3WNVTFH6XDQXC3WMPPNS7/graph.json","fetch_events":"https://pith.science/api/pith-number/37SBL3WNVTFH6XDQXC3WMPPNS7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7/action/storage_attestation","attest_author":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7/action/author_attestation","sign_citation":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7/action/citation_signature","submit_replication":"https://pith.science/pith/37SBL3WNVTFH6XDQXC3WMPPNS7/action/replication_record"}},"created_at":"2026-07-07T01:16:44.035981+00:00","updated_at":"2026-07-07T01:16:44.035981+00:00"}