{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TOECQIBNLR7JDH3VQD5WRE5Y2F","short_pith_number":"pith:TOECQIBN","schema_version":"1.0","canonical_sha256":"9b8828202d5c7e919f7580fb6893b8d1730b001c4ce52432621fcabeabaab923","source":{"kind":"arxiv","id":"2405.17743","version":5},"attestation_state":"computed","paper":{"title":"ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CE","cs.LG"],"primary_cat":"cs.CL","authors_text":"Benyou Wang, Chenyu Huang, Dongdong Ge, Ruoqing Jiang, Shixi Hu, Xin Zheng, Zhengyang Tang, Zizhuo Wang","submitted_at":"2024-05-28T01:55:35Z","abstract_excerpt":"Optimization modeling plays a critical role in the application of Operations Research (OR) tools to address real-world problems, yet they pose challenges and require extensive expertise from OR experts. With the advent of large language models (LLMs), new opportunities have emerged to streamline and automate such task. However, current research predominantly relies on closed-source LLMs such as GPT-4, along with extensive prompt engineering techniques. This reliance stems from the scarcity of high-quality training datasets for optimization modeling, resulting in elevated costs, prolonged proce"},"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":"2405.17743","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-28T01:55:35Z","cross_cats_sorted":["cs.AI","cs.CE","cs.LG"],"title_canon_sha256":"c71b1772d284c3846b3639248304006671280ea06dfe13461a7a5a0e09cf8440","abstract_canon_sha256":"e1003bfa361a7553a72aaaf9f655d950249324db999fef3aa5bba14ed5c87f49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:39.713208Z","signature_b64":"C2/4JInKG5k/1UCPERNrmZA0kTse3duZapNrXFj5rLEEWoXq7V/8JXZYYulLohysfwKUbPXkLuNfhs8h1/pdCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b8828202d5c7e919f7580fb6893b8d1730b001c4ce52432621fcabeabaab923","last_reissued_at":"2026-07-05T11:44:39.712725Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:39.712725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CE","cs.LG"],"primary_cat":"cs.CL","authors_text":"Benyou Wang, Chenyu Huang, Dongdong Ge, Ruoqing Jiang, Shixi Hu, Xin Zheng, Zhengyang Tang, Zizhuo Wang","submitted_at":"2024-05-28T01:55:35Z","abstract_excerpt":"Optimization modeling plays a critical role in the application of Operations Research (OR) tools to address real-world problems, yet they pose challenges and require extensive expertise from OR experts. With the advent of large language models (LLMs), new opportunities have emerged to streamline and automate such task. However, current research predominantly relies on closed-source LLMs such as GPT-4, along with extensive prompt engineering techniques. This reliance stems from the scarcity of high-quality training datasets for optimization modeling, resulting in elevated costs, prolonged proce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17743","kind":"arxiv","version":5},"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/2405.17743/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":"2405.17743","created_at":"2026-07-05T11:44:39.712784+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.17743v5","created_at":"2026-07-05T11:44:39.712784+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17743","created_at":"2026-07-05T11:44:39.712784+00:00"},{"alias_kind":"pith_short_12","alias_value":"TOECQIBNLR7J","created_at":"2026-07-05T11:44:39.712784+00:00"},{"alias_kind":"pith_short_16","alias_value":"TOECQIBNLR7JDH3V","created_at":"2026-07-05T11:44:39.712784+00:00"},{"alias_kind":"pith_short_8","alias_value":"TOECQIBN","created_at":"2026-07-05T11:44:39.712784+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00991","citing_title":"Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2507.14995","citing_title":"LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2509.17677","citing_title":"EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18327","citing_title":"PARM: Pipeline-Adapted Reward Model","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02728","citing_title":"ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F","json":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F.json","graph_json":"https://pith.science/api/pith-number/TOECQIBNLR7JDH3VQD5WRE5Y2F/graph.json","events_json":"https://pith.science/api/pith-number/TOECQIBNLR7JDH3VQD5WRE5Y2F/events.json","paper":"https://pith.science/paper/TOECQIBN"},"agent_actions":{"view_html":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F","download_json":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F.json","view_paper":"https://pith.science/paper/TOECQIBN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.17743&json=true","fetch_graph":"https://pith.science/api/pith-number/TOECQIBNLR7JDH3VQD5WRE5Y2F/graph.json","fetch_events":"https://pith.science/api/pith-number/TOECQIBNLR7JDH3VQD5WRE5Y2F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F/action/storage_attestation","attest_author":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F/action/author_attestation","sign_citation":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F/action/citation_signature","submit_replication":"https://pith.science/pith/TOECQIBNLR7JDH3VQD5WRE5Y2F/action/replication_record"}},"created_at":"2026-07-05T11:44:39.712784+00:00","updated_at":"2026-07-05T11:44:39.712784+00:00"}