{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KKMYMKO5EUAIDIZ6YMBMT7PGMQ","short_pith_number":"pith:KKMYMKO5","schema_version":"1.0","canonical_sha256":"52998629dd250081a33ec302c9fde6643a3a75084c95b4a37925531a46989190","source":{"kind":"arxiv","id":"2402.16269","version":1},"attestation_state":"computed","paper":{"title":"From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.AI","authors_text":"Dick den Hertog, Donato Maragno, Farzaneh Mirzazadeh, Ilker Birbil, Jannis Kurtz, Leonard Boussioux, Segev Wasserkrug","submitted_at":"2024-02-26T03:10:11Z","abstract_excerpt":"Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at the intersection of LLMs and optimization to create a Decision Optimization CoPilot (DOCP) - an AI tool designed to assist any decision maker, interacting in natural language to grasp the business problem, subsequently formulating and solvin"},"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":"2402.16269","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-02-26T03:10:11Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"fc373156ab818c65bc0b9c262db5db7afb351adf357cb84b635a646016eb52e8","abstract_canon_sha256":"1a8c59ca9a79bd8dce271a0a17d01c507faaa099350e415fe26bf8a46b6caa7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:19.676973Z","signature_b64":"GgkSLNPjywaNGK/73vtolq9Nds4JhztZhD1d+1NRQfZ0mwa4r+U/iw9M8sg+k2YOom36hMNI87910hYiDU1HDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52998629dd250081a33ec302c9fde6643a3a75084c95b4a37925531a46989190","last_reissued_at":"2026-07-05T07:49:19.676523Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:19.676523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"cs.AI","authors_text":"Dick den Hertog, Donato Maragno, Farzaneh Mirzazadeh, Ilker Birbil, Jannis Kurtz, Leonard Boussioux, Segev Wasserkrug","submitted_at":"2024-02-26T03:10:11Z","abstract_excerpt":"Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at the intersection of LLMs and optimization to create a Decision Optimization CoPilot (DOCP) - an AI tool designed to assist any decision maker, interacting in natural language to grasp the business problem, subsequently formulating and solvin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16269","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/2402.16269/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":"2402.16269","created_at":"2026-07-05T07:49:19.676585+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.16269v1","created_at":"2026-07-05T07:49:19.676585+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16269","created_at":"2026-07-05T07:49:19.676585+00:00"},{"alias_kind":"pith_short_12","alias_value":"KKMYMKO5EUAI","created_at":"2026-07-05T07:49:19.676585+00:00"},{"alias_kind":"pith_short_16","alias_value":"KKMYMKO5EUAIDIZ6","created_at":"2026-07-05T07:49:19.676585+00:00"},{"alias_kind":"pith_short_8","alias_value":"KKMYMKO5","created_at":"2026-07-05T07:49:19.676585+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02728","citing_title":"ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ","json":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ.json","graph_json":"https://pith.science/api/pith-number/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/graph.json","events_json":"https://pith.science/api/pith-number/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/events.json","paper":"https://pith.science/paper/KKMYMKO5"},"agent_actions":{"view_html":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ","download_json":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ.json","view_paper":"https://pith.science/paper/KKMYMKO5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.16269&json=true","fetch_graph":"https://pith.science/api/pith-number/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/graph.json","fetch_events":"https://pith.science/api/pith-number/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/action/storage_attestation","attest_author":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/action/author_attestation","sign_citation":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/action/citation_signature","submit_replication":"https://pith.science/pith/KKMYMKO5EUAIDIZ6YMBMT7PGMQ/action/replication_record"}},"created_at":"2026-07-05T07:49:19.676585+00:00","updated_at":"2026-07-05T07:49:19.676585+00:00"}