{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QAYRWMIEWC3YSCSA3TTF24H64U","short_pith_number":"pith:QAYRWMIE","schema_version":"1.0","canonical_sha256":"80311b3104b0b7890a40dce65d70fee50345e853a5f3af08942e8f715fe7b22c","source":{"kind":"arxiv","id":"2302.04643","version":1},"attestation_state":"computed","paper":{"title":"A Novel Approach for Auto-Formulation of Optimization Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Enhong Chen, Jiayu Liu, Jinze Wu, Longhu Qin, Qi Liu, Shangzi Xue, Tong Xiao, Yuting Ning, Zhenya Huang","submitted_at":"2023-02-09T13:57:06Z","abstract_excerpt":"In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem. In this paper, we present the solution of our team. First, we treat subtask 1 as a named entity recognition (NER) problem with the solution pipeline including pre-processing methods, adversarial training, post-processing methods and ensemble learn"},"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":"2302.04643","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-09T13:57:06Z","cross_cats_sorted":[],"title_canon_sha256":"39f3db56b264961a6629c1eaa28b928e7ddfba9ca1ab5f5f65de944cff1d2a18","abstract_canon_sha256":"529358aa6f9c263747482e629187e801ca0c8c4fef87e0c0f3f7a91216714b71"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:16.727438Z","signature_b64":"B7j7xZQxclP/0W9vFcC2f4CSMfUEa7qZi5mVO1mEwnSGJ2sF5VLSaVRM47XTtIZVrv77j7ULCn7pBvEg4JDuCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80311b3104b0b7890a40dce65d70fee50345e853a5f3af08942e8f715fe7b22c","last_reissued_at":"2026-07-05T05:40:16.726943Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:16.726943Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Novel Approach for Auto-Formulation of Optimization Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Enhong Chen, Jiayu Liu, Jinze Wu, Longhu Qin, Qi Liu, Shangzi Xue, Tong Xiao, Yuting Ning, Zhenya Huang","submitted_at":"2023-02-09T13:57:06Z","abstract_excerpt":"In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem. In this paper, we present the solution of our team. First, we treat subtask 1 as a named entity recognition (NER) problem with the solution pipeline including pre-processing methods, adversarial training, post-processing methods and ensemble learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04643","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/2302.04643/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":"2302.04643","created_at":"2026-07-05T05:40:16.727002+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.04643v1","created_at":"2026-07-05T05:40:16.727002+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04643","created_at":"2026-07-05T05:40:16.727002+00:00"},{"alias_kind":"pith_short_12","alias_value":"QAYRWMIEWC3Y","created_at":"2026-07-05T05:40:16.727002+00:00"},{"alias_kind":"pith_short_16","alias_value":"QAYRWMIEWC3YSCSA","created_at":"2026-07-05T05:40:16.727002+00:00"},{"alias_kind":"pith_short_8","alias_value":"QAYRWMIE","created_at":"2026-07-05T05:40:16.727002+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.00369","citing_title":"InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00369","citing_title":"InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees","ref_index":59,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U","json":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U.json","graph_json":"https://pith.science/api/pith-number/QAYRWMIEWC3YSCSA3TTF24H64U/graph.json","events_json":"https://pith.science/api/pith-number/QAYRWMIEWC3YSCSA3TTF24H64U/events.json","paper":"https://pith.science/paper/QAYRWMIE"},"agent_actions":{"view_html":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U","download_json":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U.json","view_paper":"https://pith.science/paper/QAYRWMIE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.04643&json=true","fetch_graph":"https://pith.science/api/pith-number/QAYRWMIEWC3YSCSA3TTF24H64U/graph.json","fetch_events":"https://pith.science/api/pith-number/QAYRWMIEWC3YSCSA3TTF24H64U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U/action/storage_attestation","attest_author":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U/action/author_attestation","sign_citation":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U/action/citation_signature","submit_replication":"https://pith.science/pith/QAYRWMIEWC3YSCSA3TTF24H64U/action/replication_record"}},"created_at":"2026-07-05T05:40:16.727002+00:00","updated_at":"2026-07-05T05:40:16.727002+00:00"}