{"as_of":"2026-08-12T18:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aadec3357b6182bd0b23b96ad36f168a7b33a4000a81aa09132ee9bd81b2ea55","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T02:31:51.934903Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.00015/citation-record","integrity":"/paper/2608.00015/integrity","json":"/paper/2608.00015/citation-record.json","paper":"/paper/2608.00015"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.881775Z","title":"Ramamonjison, T","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.881775Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:662fa00715d921720b0671b5dc9cd9d34eca125b1eff54465e04d647e218a1bd","observation_id":"d00182c4-0c61-4622-937c-c34264c0ffc0","resolution":{"observed_at":"2026-08-04T02:31:51.881775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.886164Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.886164Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:f42946f90a1d808c897be79d0a519e31ce4a5dedf186a248eca106dfd22603d4","observation_id":"99cb80cb-6835-4bb7-8b04-1aa25989a993","resolution":{"observed_at":"2026-08-04T02:31:51.886164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.889363Z","title":"Izacard and E","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.889363Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:4766658fb64bcf8f763cf037d96b461d0bb93b27cabb582b86318382a055f5e3","observation_id":"dfeefa2f-5c58-46e9-a43a-01cdc791de15","resolution":{"observed_at":"2026-08-04T02:31:51.889363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.892464Z","title":"Karpukhin, B","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.892464Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:117342db27d32193cc450bfbdc0eeb70d924a7d6526a3a428a23a96f714fae3c","observation_id":"a96748fa-7ca0-48a8-b901-64e8df4f1bd1","resolution":{"observed_at":"2026-08-04T02:31:51.892464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.895645Z","title":"Lewis, E","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.895645Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:a5d13ed3fa68a5370be6d5a6cc0255f49ad9ac7bf967c27bd79fa12372c34b1c","observation_id":"f82f0b66-cf9f-4e2a-9c7e-bca48ab076f4","resolution":{"observed_at":"2026-08-04T02:31:51.895645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.899265Z","title":"Reimers and I","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.899265Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:ceae1e5077e5a4aa5edd4d526397175c73afcd99a205a1dbdd97e05cc618b208","observation_id":"7bfbc3ba-273d-4b7b-9d95-cbf78452e337","resolution":{"observed_at":"2026-08-04T02:31:51.899265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.902681Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.902681Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:4dc2e53f76e2c053142c95604015c5e330b70e93c486181a069a77ec4b9ebb38","observation_id":"4333ffdb-9206-4f27-b630-9c98c54aced5","resolution":{"observed_at":"2026-08-04T02:31:51.902681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03629","last_updated":"2023-03-10T01:00:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-10-06T01:00:32Z","title":"ReAct: Synergizing Reasoning and Acting in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03629","snapshot_observed_at":"2026-08-04T02:31:51.905634Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.905634Z"},"links":{"cited_paper":"/paper/2210.03629","citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:8b47059bccfc2cbf8ef897df7c8c09f4596d224a69e977b9d81bf5b973a9363a","observation_id":"f9e716d3-4b48-4fea-b0d8-1b9d894e4cfb","resolution":{"observed_at":"2026-08-04T02:31:51.905634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06116","last_updated":"2023-10-30T18:23:45Z","snapshot_observed_at":"2026-08-11T03:55:20.095416Z","submitted_at":"2023-10-09T19:47:03Z","title":"OptiMUS: Optimization Modeling Using MIP Solvers and large language models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06116","snapshot_observed_at":"2026-08-04T02:31:51.909472Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.909472Z"},"links":{"cited_paper":"/paper/2310.06116","citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:d4efd9d06dc75b7213f4af72eeb4ff88b39b7f3d43176f2ed6791c9ac29124e2","observation_id":"44773237-5c69-4c68-b205-9f8e418f314d","resolution":{"observed_at":"2026-08-04T02:31:51.909472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.913101Z","title":"Bradbury et al","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.913101Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:da38be2af50d938d22572ec7a7333fcc62ee391639b0732d63dfcc978c0210a5","observation_id":"fd159e26-e3e2-406a-b3f7-dd70094838f4","resolution":{"observed_at":"2026-08-04T02:31:51.913101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.915963Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.915963Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:96ae3766ee25617c08c0f955fd89e5fe1bae03ea866487d09f2788086b0cc430","observation_id":"4a32aa46-190d-4367-8953-70f508748d13","resolution":{"observed_at":"2026-08-04T02:31:51.915963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.918864Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.918864Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:252ee82f9d697e5081c985a90b7efc5d64e39486ec0935d5c5c511a18e63269f","observation_id":"e308eec9-e1c0-4951-9c82-13d42d589167","resolution":{"observed_at":"2026-08-04T02:31:51.918864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.921828Z","title":"Jeong, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.921828Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:a3c900595fa5a566c4d3e0f823f53878d5b3e99843b5a91200d5df136816f2f6","observation_id":"cbaafb65-75c1-41ce-8963-70d66ca538f5","resolution":{"observed_at":"2026-08-04T02:31:51.921828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17743","last_updated":"2025-04-04T13:31:38Z","snapshot_observed_at":"2026-08-10T20:17:46.245958Z","submitted_at":"2024-05-28T01:55:35Z","title":"ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17743","snapshot_observed_at":"2026-08-04T02:31:51.925566Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.925566Z"},"links":{"cited_paper":"/paper/2405.17743","citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:16fe7a03a27a44de7463a05eb8d28268980297ad7eaecd4653519c56b685df43","observation_id":"5a9ff112-2b6e-4f97-b4b4-6d0b6085f0cc","resolution":{"observed_at":"2026-08-04T02:31:51.925566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.929094Z","title":"Salminen, D","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.929094Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:0717a16ca027fe0d771be2d34edfb63c67cfde7110214124cc7e0f8749d21962","observation_id":"e9e3aae6-e899-40ff-b971-15b26d7c73b5","resolution":{"observed_at":"2026-08-04T02:31:51.929094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10009","last_updated":"2025-08-01T04:52:21Z","snapshot_observed_at":"2026-08-07T17:08:15.599661Z","submitted_at":"2025-03-13T03:40:50Z","title":"OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.10009","snapshot_observed_at":"2026-08-04T02:31:51.931899Z","title":"Yao and K","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.931899Z"},"links":{"cited_paper":"/paper/2503.10009","citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:26653ca41630d8dff8ba6d73d54fddfd7039350cf5d81fd366f8894bf69b807a","observation_id":"2ef090ce-8a0e-4285-9b10-e15a7d088729","resolution":{"observed_at":"2026-08-04T02:31:51.931899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T02:31:51.934903Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T02:31:51.934903Z"},"links":{"citing_paper":"/paper/2608.00015"},"observation_digest":"sha256:e44502f49f4e66ca2fe103ff19017b42489a21ee57a1a22cfdc46262d96a039a","observation_id":"1efd16ec-38ec-4b6d-bf15-94797b3a32c8","resolution":{"observed_at":"2026-08-04T02:31:51.934903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.00015","last_updated":"2026-06-24T23:55:35Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-06T23:11:31.305261Z","submitted_at":"2026-06-24T23:55:35Z","title":"Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":17},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2608.00015."}