{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KGUCBMLA2WNAHKAJPD2GXGQ47E","short_pith_number":"pith:KGUCBMLA","schema_version":"1.0","canonical_sha256":"51a820b160d59a03a80978f46b9a1cf93e23bcd7b14ae79bbcc5d86e4ab86d67","source":{"kind":"arxiv","id":"2605.31049","version":1},"attestation_state":"computed","paper":{"title":"Learning to Solve and Optimize by Evolving Code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LO"],"primary_cat":"cs.LG","authors_text":"Benedetta Strizzolo, Francesco Zuccato, Gerhard Friedrich, Konstantin Schekotihin, Patrick Rodler, Veronika Semmelrock","submitted_at":"2026-05-29T09:25:07Z","abstract_excerpt":"Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful problem formalization, specialized solvers, and expert-designed heuristics. Thus, experts need to specify not only what solutions are, but also how they are derived. By introducing the tool CHECKMATE, we show that algorithm generation via code evolution represents a paradigm shift by eliminating the need to formulate the how. CHECKMATE solely relies on the what. Specifically, a formal specification ensures solutions' co"},"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":"2605.31049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T09:25:07Z","cross_cats_sorted":["cs.AI","cs.LO"],"title_canon_sha256":"1788f7078d35918bc560024393517a7470ca029b8969c7d8cc81ea1669657332","abstract_canon_sha256":"5a47a4cc7ff7dca538c22af735e0c7ed3e10b9931505963d83bf7a77cc29c0ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:32.854897Z","signature_b64":"clzo8y+eOdb9rAcEbLmFkyfqvd7x8E7Yhe+z0AhEyl4QDChuuhUo2JA4Hxc3boP0ukSuZ7AJ2pnK5fSPYvykCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51a820b160d59a03a80978f46b9a1cf93e23bcd7b14ae79bbcc5d86e4ab86d67","last_reissued_at":"2026-06-01T01:03:32.854008Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:32.854008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Solve and Optimize by Evolving Code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LO"],"primary_cat":"cs.LG","authors_text":"Benedetta Strizzolo, Francesco Zuccato, Gerhard Friedrich, Konstantin Schekotihin, Patrick Rodler, Veronika Semmelrock","submitted_at":"2026-05-29T09:25:07Z","abstract_excerpt":"Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful problem formalization, specialized solvers, and expert-designed heuristics. Thus, experts need to specify not only what solutions are, but also how they are derived. By introducing the tool CHECKMATE, we show that algorithm generation via code evolution represents a paradigm shift by eliminating the need to formulate the how. CHECKMATE solely relies on the what. Specifically, a formal specification ensures solutions' co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31049","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/2605.31049/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":"2605.31049","created_at":"2026-06-01T01:03:32.854169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31049v1","created_at":"2026-06-01T01:03:32.854169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31049","created_at":"2026-06-01T01:03:32.854169+00:00"},{"alias_kind":"pith_short_12","alias_value":"KGUCBMLA2WNA","created_at":"2026-06-01T01:03:32.854169+00:00"},{"alias_kind":"pith_short_16","alias_value":"KGUCBMLA2WNAHKAJ","created_at":"2026-06-01T01:03:32.854169+00:00"},{"alias_kind":"pith_short_8","alias_value":"KGUCBMLA","created_at":"2026-06-01T01:03:32.854169+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/KGUCBMLA2WNAHKAJPD2GXGQ47E","json":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E.json","graph_json":"https://pith.science/api/pith-number/KGUCBMLA2WNAHKAJPD2GXGQ47E/graph.json","events_json":"https://pith.science/api/pith-number/KGUCBMLA2WNAHKAJPD2GXGQ47E/events.json","paper":"https://pith.science/paper/KGUCBMLA"},"agent_actions":{"view_html":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E","download_json":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E.json","view_paper":"https://pith.science/paper/KGUCBMLA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31049&json=true","fetch_graph":"https://pith.science/api/pith-number/KGUCBMLA2WNAHKAJPD2GXGQ47E/graph.json","fetch_events":"https://pith.science/api/pith-number/KGUCBMLA2WNAHKAJPD2GXGQ47E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E/action/storage_attestation","attest_author":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E/action/author_attestation","sign_citation":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E/action/citation_signature","submit_replication":"https://pith.science/pith/KGUCBMLA2WNAHKAJPD2GXGQ47E/action/replication_record"}},"created_at":"2026-06-01T01:03:32.854169+00:00","updated_at":"2026-06-01T01:03:32.854169+00:00"}