{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QQR3NP77QAHJNGCEMFH6KYH7CJ","short_pith_number":"pith:QQR3NP77","schema_version":"1.0","canonical_sha256":"8423b6bfff800e969844614fe560ff124add3fdf44509670f2070c0c482e7833","source":{"kind":"arxiv","id":"2606.00718","version":1},"attestation_state":"computed","paper":{"title":"LLM-Driven Co-Evolutionary Automated Heuristic Design for Bi-Component Coupled Combinatorial Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.AI","authors_text":"Jialong Shi, Jianyong Sun, Mingen Kuang, Xi Lin, Xudong Deng, Ye Fan","submitted_at":"2026-05-30T13:04:51Z","abstract_excerpt":"While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuristics as a single operator or search strategy, limiting their ability to model strong coupling among multiple decision substructures in problems such as the Traveling Thief Problem (TTP) and the Traveling Purchaser Problem (TPP). In this work, we propose CoEvo-AHD, an LLM-driven dual-population co-evolutionary framework for automated heuristic design in coupled combinatorial optimization. Unlike prior methods that evolve individual heuristics i"},"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":"2606.00718","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-30T13:04:51Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"4b4a7915a075651d1cdd04322e52f55fe42cd4afa3934c534b1f8a86378dd359","abstract_canon_sha256":"8a7f7491842f7da6e0ab132ef8b62fcc64d16ce9dd089108ad0673c49266884e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:04:03.673531Z","signature_b64":"TK/gMFe0mV34gm+uelsFiaqF8d9lTohBde4owL+ywW1WwmMUTQW80WrZL0IjvRKO/kzhHfv99d0sMRivvZJqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8423b6bfff800e969844614fe560ff124add3fdf44509670f2070c0c482e7833","last_reissued_at":"2026-06-02T01:04:03.673120Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:04:03.673120Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLM-Driven Co-Evolutionary Automated Heuristic Design for Bi-Component Coupled Combinatorial Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.AI","authors_text":"Jialong Shi, Jianyong Sun, Mingen Kuang, Xi Lin, Xudong Deng, Ye Fan","submitted_at":"2026-05-30T13:04:51Z","abstract_excerpt":"While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuristics as a single operator or search strategy, limiting their ability to model strong coupling among multiple decision substructures in problems such as the Traveling Thief Problem (TTP) and the Traveling Purchaser Problem (TPP). In this work, we propose CoEvo-AHD, an LLM-driven dual-population co-evolutionary framework for automated heuristic design in coupled combinatorial optimization. Unlike prior methods that evolve individual heuristics i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00718","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/2606.00718/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":"2606.00718","created_at":"2026-06-02T01:04:03.673172+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00718v1","created_at":"2026-06-02T01:04:03.673172+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00718","created_at":"2026-06-02T01:04:03.673172+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQR3NP77QAHJ","created_at":"2026-06-02T01:04:03.673172+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQR3NP77QAHJNGCE","created_at":"2026-06-02T01:04:03.673172+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQR3NP77","created_at":"2026-06-02T01:04:03.673172+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/QQR3NP77QAHJNGCEMFH6KYH7CJ","json":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ.json","graph_json":"https://pith.science/api/pith-number/QQR3NP77QAHJNGCEMFH6KYH7CJ/graph.json","events_json":"https://pith.science/api/pith-number/QQR3NP77QAHJNGCEMFH6KYH7CJ/events.json","paper":"https://pith.science/paper/QQR3NP77"},"agent_actions":{"view_html":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ","download_json":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ.json","view_paper":"https://pith.science/paper/QQR3NP77","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00718&json=true","fetch_graph":"https://pith.science/api/pith-number/QQR3NP77QAHJNGCEMFH6KYH7CJ/graph.json","fetch_events":"https://pith.science/api/pith-number/QQR3NP77QAHJNGCEMFH6KYH7CJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ/action/storage_attestation","attest_author":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ/action/author_attestation","sign_citation":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ/action/citation_signature","submit_replication":"https://pith.science/pith/QQR3NP77QAHJNGCEMFH6KYH7CJ/action/replication_record"}},"created_at":"2026-06-02T01:04:03.673172+00:00","updated_at":"2026-06-02T01:04:03.673172+00:00"}