{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RRBWCNS72ZH3UFPF2CHUC6QDKL","short_pith_number":"pith:RRBWCNS7","schema_version":"1.0","canonical_sha256":"8c4361365fd64fba15e5d08f417a0352defeaf8f894190afa9a42f5585f817fd","source":{"kind":"arxiv","id":"2404.12638","version":1},"attestation_state":"computed","paper":{"title":"Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fangzhou Zhu, Feng Wu, Jia Zeng, Jie Wang, Mingxuan Yuan, Xijun Li, Yongdong Zhang, Yufei Kuang, Zhihai Wang, Zhihao Shi","submitted_at":"2024-04-19T05:40:25Z","abstract_excerpt":"Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential to learn more effective heuristics. However, many existing learning-based methods learn which cuts to prefer, neglecting the importance of learning how many cuts to select. Moreover, we observe that (P3) what order of selected cuts to prefer"},"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":"2404.12638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-04-19T05:40:25Z","cross_cats_sorted":[],"title_canon_sha256":"61c827429219bb3da8f659060aaaccf0220c2fc74f6c569337ed6185685ffa08","abstract_canon_sha256":"c2610e324012bd2ae10913ebe132366dfa488af3a91a3b748e01b4c89a7712c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:09:56.671701Z","signature_b64":"Va1tU8Sy+YVYmtPd5Oj/gmQ4OC/Zvxg065QlK4c5+yIUAiJWkyhsYjJkRUlJn1lBgKKNcXeFl/rG85wsaOh2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c4361365fd64fba15e5d08f417a0352defeaf8f894190afa9a42f5585f817fd","last_reissued_at":"2026-07-05T08:09:56.671313Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:09:56.671313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fangzhou Zhu, Feng Wu, Jia Zeng, Jie Wang, Mingxuan Yuan, Xijun Li, Yongdong Zhang, Yufei Kuang, Zhihai Wang, Zhihao Shi","submitted_at":"2024-04-19T05:40:25Z","abstract_excerpt":"Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends on (P1) which cuts to prefer and (P2) how many cuts to select. Although modern MILP solvers tackle (P1)-(P2) by human-designed heuristics, machine learning carries the potential to learn more effective heuristics. However, many existing learning-based methods learn which cuts to prefer, neglecting the importance of learning how many cuts to select. Moreover, we observe that (P3) what order of selected cuts to prefer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.12638","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/2404.12638/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":"2404.12638","created_at":"2026-07-05T08:09:56.671392+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.12638v1","created_at":"2026-07-05T08:09:56.671392+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.12638","created_at":"2026-07-05T08:09:56.671392+00:00"},{"alias_kind":"pith_short_12","alias_value":"RRBWCNS72ZH3","created_at":"2026-07-05T08:09:56.671392+00:00"},{"alias_kind":"pith_short_16","alias_value":"RRBWCNS72ZH3UFPF","created_at":"2026-07-05T08:09:56.671392+00:00"},{"alias_kind":"pith_short_8","alias_value":"RRBWCNS7","created_at":"2026-07-05T08:09:56.671392+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/RRBWCNS72ZH3UFPF2CHUC6QDKL","json":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL.json","graph_json":"https://pith.science/api/pith-number/RRBWCNS72ZH3UFPF2CHUC6QDKL/graph.json","events_json":"https://pith.science/api/pith-number/RRBWCNS72ZH3UFPF2CHUC6QDKL/events.json","paper":"https://pith.science/paper/RRBWCNS7"},"agent_actions":{"view_html":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL","download_json":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL.json","view_paper":"https://pith.science/paper/RRBWCNS7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.12638&json=true","fetch_graph":"https://pith.science/api/pith-number/RRBWCNS72ZH3UFPF2CHUC6QDKL/graph.json","fetch_events":"https://pith.science/api/pith-number/RRBWCNS72ZH3UFPF2CHUC6QDKL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL/action/storage_attestation","attest_author":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL/action/author_attestation","sign_citation":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL/action/citation_signature","submit_replication":"https://pith.science/pith/RRBWCNS72ZH3UFPF2CHUC6QDKL/action/replication_record"}},"created_at":"2026-07-05T08:09:56.671392+00:00","updated_at":"2026-07-05T08:09:56.671392+00:00"}