{"as_of":"2026-08-18T03:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aa1e096584c64f04258c9ffd17d8b873a8a9afe34a5f149fb5f15da2df8c6232","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:34:30.298591Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.04354/citation-record","integrity":"/paper/2505.04354/integrity","json":"/paper/2505.04354/citation-record.json","paper":"/paper/2505.04354"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.17238","last_updated":"2024-10-22T17:56:08Z","snapshot_observed_at":"2026-08-16T13:06:53.507544Z","submitted_at":"2024-10-22T17:56:08Z","title":"SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.17238","snapshot_observed_at":"2026-08-15T23:34:30.081124Z","title":"SELA: Tree-search enhanced llm agents for automated machine learning.arXiv:2410.17238,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.081124Z"},"links":{"cited_paper":"/paper/2410.17238","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:282a101569c052db524b82d32856feecc0f803b0e89227e54a5b2fe92a4a42dc","observation_id":"7bba3d98-dc31-462d-8562-997d1fb64485","resolution":{"observed_at":"2026-08-15T23:34:30.081124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15402","last_updated":"2024-06-06T01:58:54Z","snapshot_observed_at":"2026-08-16T14:57:18.396629Z","submitted_at":"2023-09-27T04:53:10Z","title":"Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15402","snapshot_observed_at":"2026-08-15T23:34:30.086049Z","title":"A survey of chain of thought reasoning: Advances, frontiers and future","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.086049Z"},"links":{"cited_paper":"/paper/2309.15402","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:e97990e1e6d3a4762c4128826e29a78974abcc79b9d4fbceb0ac10f980ca3c9a","observation_id":"8433d15b-47df-4ea9-a751-c5f2ff9eed35","resolution":{"observed_at":"2026-08-15T23:34:30.086049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.03244","last_updated":"2024-03-26T20:35:45Z","snapshot_observed_at":"2026-08-16T14:29:25.680973Z","submitted_at":"2024-01-06T15:55:14Z","title":"Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.03244","snapshot_observed_at":"2026-08-15T23:34:30.096302Z","title":"Artificial intelligence for operations research: Revolutionizing the operations research process","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.096302Z"},"links":{"cited_paper":"/paper/2401.03244","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:8a9efa9899ff1742119b2c5c518315b3de7ea00a48f50e799138b080a8c8c8c3","observation_id":"ee2d239f-945f-4755-807e-225423b407f6","resolution":{"observed_at":"2026-08-15T23:34:30.096302Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:34:31.085433Z","title":"MIPLIB 2017: Data-driven compilation of the 6th mixed-integer programming library","venue":null,"work_id":"6b9aa314-10b2-4f78-ad67-034dc0e5b9c5","year":2017},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.100591Z"},"links":{"citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:ff70c2707079799eff1903d3eaddbc77921650cfb90072e75f56cedb1835b58f","observation_id":"5108df4d-98b3-4b6e-a503-e75e720b0184","resolution":{"observed_at":"2026-08-15T23:34:31.090083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-15T23:34:30.109333Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.109333Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:095e8ea7632b385807cf852b158fcb78f079bd38dcc4aa10bda3554797f3e5bf","observation_id":"576fba0c-ed7f-4cf4-998a-0cedb23af8bb","resolution":{"observed_at":"2026-08-15T23:34:30.109333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13144","last_updated":"2025-02-15T13:45:56Z","snapshot_observed_at":"2026-08-16T13:50:51.185886Z","submitted_at":"2024-05-21T18:29:54Z","title":"LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13144","snapshot_observed_at":"2026-08-15T23:34:30.122117Z","title":"MAMO: a mathematical modeling benchmark with solvers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.122117Z"},"links":{"cited_paper":"/paper/2405.13144","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:2bb7580dcdf4cce6766bc6e6b03170e911e6f3cd976442cb4a4b16091cb153e0","observation_id":"1ec9202f-2527-4281-b28d-1ae55981aa3d","resolution":{"observed_at":"2026-08-15T23:34:30.122117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.01984","last_updated":"2023-01-05T09:39:24Z","snapshot_observed_at":"2026-08-17T01:46:14.016277Z","submitted_at":"2023-01-05T09:39:24Z","title":"The Evolutionary Computation Methods No One Should Use","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.01984","snapshot_observed_at":"2026-08-15T23:34:30.130734Z","title":"The evolutionary computation methods no one should use","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.130734Z"},"links":{"cited_paper":"/paper/2301.01984","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:bee9f9f57c8af9c3270338324576cf6e153c1ef6c83b1eda544c6533af32a56d","observation_id":"47cfdc2b-35bf-4343-9fae-5151b537ddb7","resolution":{"observed_at":"2026-08-15T23:34:30.130734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18806","last_updated":"2025-03-24T15:49:32Z","snapshot_observed_at":"2026-08-17T16:51:21.101365Z","submitted_at":"2025-03-24T15:49:32Z","title":"Formalization of Algorithms for Optimization with Block Structures","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.18806","snapshot_observed_at":"2026-08-15T23:34:30.135309Z","title":"Formalization of algorithms for optimization with block structures","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.135309Z"},"links":{"cited_paper":"/paper/2503.18806","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:3e1cdc479d4d9fceedd5ea6499ee21c23d5130577d4aa4371a21ad712067a841","observation_id":"a9588bec-9dab-4e11-af5a-f10e7082b657","resolution":{"observed_at":"2026-08-15T23:34:30.135309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15271","last_updated":"2023-11-26T11:50:56Z","snapshot_observed_at":"2026-08-16T14:40:20.049428Z","submitted_at":"2023-11-26T11:50:56Z","title":"Synthesizing mixed-integer linear programming models from natural language descriptions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15271","snapshot_observed_at":"2026-08-15T23:34:30.139643Z","title":"Synthesizing mixed-integer linear programming models from natural language descriptions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.139643Z"},"links":{"cited_paper":"/paper/2311.15271","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:b1cfea9a444e21660a493fd69bf70ca11836114b46caf008bed80743c7f410c4","observation_id":"abfc01c4-1de5-462c-8d90-0153ae50fbb1","resolution":{"observed_at":"2026-08-15T23:34:30.139643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17419","last_updated":"2025-06-25T02:24:46Z","snapshot_observed_at":"2026-08-10T09:42:17.185681Z","submitted_at":"2025-02-24T18:50:52Z","title":"From System 1 to System 2: A Survey of Reasoning Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.17419","snapshot_observed_at":"2026-08-15T23:34:30.144164Z","title":"Multi-agent credit assignment with pretrained language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.144164Z"},"links":{"cited_paper":"/paper/2502.17419","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:1a7a4ec676b9ecb05b85263281a8dedfa8cf05a433675e1f9161a70303bc9f68","observation_id":"6a37b878-0e74-471f-82fb-876da5ed87d5","resolution":{"observed_at":"2026-08-15T23:34:30.144164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15249","last_updated":"2023-11-26T09:38:44Z","snapshot_observed_at":"2026-08-16T14:40:20.755560Z","submitted_at":"2023-11-26T09:38:44Z","title":"Algorithm Evolution Using Large Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15249","snapshot_observed_at":"2026-08-15T23:34:30.148702Z","title":"Algorithm evolution using large language model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.148702Z"},"links":{"cited_paper":"/paper/2311.15249","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:8a23cf551d6d76c2657bfe0428abea0363b6be43e1404b1d943005f0112fe2f0","observation_id":"c1b6d650-e563-44eb-9c65-dff1b37bf1a5","resolution":{"observed_at":"2026-08-15T23:34:30.148702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02051","last_updated":"2024-06-01T16:48:37Z","snapshot_observed_at":"2026-08-16T14:29:57.511691Z","submitted_at":"2024-01-04T04:11:59Z","title":"Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02051","snapshot_observed_at":"2026-08-15T23:34:30.152964Z","title":"Evolution of heuristics: Towards efficient automatic algorithm design using large language model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.152964Z"},"links":{"cited_paper":"/paper/2401.02051","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:86978874087bc445a049115afccf968f61674d49d7898651e4427a847b7cf9d9","observation_id":"00893f39-7a5d-445c-ab7e-b5af52621e31","resolution":{"observed_at":"2026-08-15T23:34:30.152964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00625","last_updated":"2025-04-30T10:28:22Z","snapshot_observed_at":"2026-08-16T13:03:46.744982Z","submitted_at":"2024-11-01T14:32:19Z","title":"Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00625","snapshot_observed_at":"2026-08-15T23:34:30.161905Z","title":"J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.161905Z"},"links":{"cited_paper":"/paper/2411.00625","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:cce25d5dc4bad5127aa870f305a5fe3d7f6eea3bb1688a4c91843a968a708627","observation_id":"15ae6458-7b64-4b08-9206-7ce58f299267","resolution":{"observed_at":"2026-08-15T23:34:30.161905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20302","last_updated":"2025-01-02T23:08:47Z","snapshot_observed_at":"2026-08-16T20:31:05.765778Z","submitted_at":"2024-10-27T00:50:30Z","title":"Sequential Large Language Model-Based Hyper-parameter Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20302","snapshot_observed_at":"2026-08-15T23:34:30.165996Z","title":"Sequential large language model-based hyper-parameter optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.165996Z"},"links":{"cited_paper":"/paper/2410.20302","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:66194999cc67d350f538ec061f45f1e469fea6cba6aac6290fe70691e19ba832","observation_id":"4fa99b72-46c4-40e6-9c45-cfd3468a4971","resolution":{"observed_at":"2026-08-15T23:34:30.165996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.15231","last_updated":"2024-04-08T11:09:35Z","snapshot_observed_at":"2026-08-16T16:56:35.718642Z","submitted_at":"2022-05-30T16:41:58Z","title":"A Survey in Mathematical Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.15231","snapshot_observed_at":"2026-08-15T23:34:30.170077Z","title":"and Freitas, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.170077Z"},"links":{"cited_paper":"/paper/2205.15231","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:ca27b2e3fead86719e664e951635d1f69358c322dfe816e0ac0d9596b42d8eb7","observation_id":"dfc537a2-2269-4413-ba2b-7e9104da7f11","resolution":{"observed_at":"2026-08-15T23:34:30.170077Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05229","last_updated":"2025-08-27T16:24:39Z","snapshot_observed_at":"2026-08-16T08:54:56.543625Z","submitted_at":"2024-10-07T17:36:37Z","title":"GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05229","snapshot_observed_at":"2026-08-15T23:34:30.174627Z","title":"Gsm- symbolic: Understanding the limitations of mathematical reasoning in large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.174627Z"},"links":{"cited_paper":"/paper/2410.05229","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:7e1f0f4ffc3ec43896b73612b1c0440792e439f95f60fc53e877f2e54a0cdbe4","observation_id":"61ee98e0-d2d1-4b8a-912b-40166ee2d193","resolution":{"observed_at":"2026-08-15T23:34:30.174627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15565","last_updated":"2022-10-11T21:12:00Z","snapshot_observed_at":"2026-08-17T13:17:30.391370Z","submitted_at":"2022-09-30T16:24:36Z","title":"Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15565","snapshot_observed_at":"2026-08-15T23:34:30.184559Z","title":"T., He, S., Rengan, V ., Banitalebi-Dehkordi, A., Zhou, Z., and Zhang, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.184559Z"},"links":{"cited_paper":"/paper/2209.15565","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:a4f4fbb43b45a2fc9978b29cb6eef2174625e6e9a2cbf8c3c174bd764b553796","observation_id":"bd3c66de-56fd-4525-b743-7edd5da46aed","resolution":{"observed_at":"2026-08-15T23:34:30.184559Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:34:31.069345Z","title":"NL4OPT competition: Formulating optimization problems based on their natural language descriptions","venue":null,"work_id":"ca1ca8ba-993c-45e2-ba31-cdea3279df40","year":2022},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.188960Z"},"links":{"citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:2bd54d35a02a8b4e834da477c2027a4ff5e4bac1ab84af72c8177876df03d62c","observation_id":"2675a188-5ce6-48a4-a1aa-24f3022e7599","resolution":{"observed_at":"2026-08-15T23:34:31.074659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12950","last_updated":"2024-01-31T19:47:26Z","snapshot_observed_at":"2026-08-13T04:25:38.282910Z","submitted_at":"2023-08-24T17:39:13Z","title":"Code Llama: Open Foundation Models for Code","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12950","snapshot_observed_at":"2026-08-15T23:34:30.194056Z","title":"E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.194056Z"},"links":{"cited_paper":"/paper/2308.12950","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:7e39986c0e507f1fa5bab482ad2317e03278b3273ede94a1b9bd8ea8781f24db","observation_id":"18b85245-88f5-448c-b08f-5122a276fb9b","resolution":{"observed_at":"2026-08-15T23:34:30.194056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.04747","last_updated":"2017-06-15T13:21:04Z","snapshot_observed_at":"2026-08-15T03:49:17.013617Z","submitted_at":"2016-09-15T17:32:34Z","title":"An overview of gradient descent optimization algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.04747","snapshot_observed_at":"2026-08-15T23:34:30.198645Z","title":"An overview of gradient descent optimization algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.198645Z"},"links":{"cited_paper":"/paper/1609.04747","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:c8f8db0d45690d76f3655ea9b3c12735b88c383ee06b6b8a25d822e6ad05e8ed","observation_id":"7d099c10-b6df-4792-bcc1-db950e49a0e7","resolution":{"observed_at":"2026-08-15T23:34:30.198645Z","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":"2026-08-17T16:49:20.212163Z","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":"2026-08-15T23:34:30.212835Z","title":"ORLM: Training large language models for optimization modeling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.212835Z"},"links":{"cited_paper":"/paper/2405.17743","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:15e477b75988046610ab6def41126491d4ff211d9b80770f336bbe5ad9e9e964","observation_id":"f23c64e8-1801-4c8e-ab10-9632700c4a7e","resolution":{"observed_at":"2026-08-15T23:34:30.212835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02958","last_updated":"2025-06-06T10:13:12Z","snapshot_observed_at":"2026-08-16T13:12:48.058785Z","submitted_at":"2024-10-03T20:01:09Z","title":"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02958","snapshot_observed_at":"2026-08-15T23:34:30.217268Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.217268Z"},"links":{"cited_paper":"/paper/2410.02958","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:7cc6cb04fb6621b61f7886096eafa3ee2c95a9fd6fc2b691cbd2bb318b61c4d3","observation_id":"4a73bfd8-0702-4af8-8c7c-be72bc4e4681","resolution":{"observed_at":"2026-08-15T23:34:30.217268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01589","last_updated":"2023-08-03T07:48:02Z","snapshot_observed_at":"2026-08-16T15:11:08.174548Z","submitted_at":"2023-08-03T07:48:02Z","title":"Holy Grail 2.0: From Natural Language to Constraint Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01589","snapshot_observed_at":"2026-08-15T23:34:30.221747Z","title":"Holy Grail 2.0: From natural language to constraint models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.221747Z"},"links":{"cited_paper":"/paper/2308.01589","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:c5bda48dacf97e63be41f26df807ce71d184e809c3cdae1464bbbe451ea6f5c8","observation_id":"711cae60-790e-46f7-a976-1784fce08b19","resolution":{"observed_at":"2026-08-15T23:34:30.221747Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:34:31.054302Z","title":"Large language models still can’t plan (a benchmark for LLMs on planning and reasoning about change)","venue":null,"work_id":"3c3e19a1-c6ab-4877-ad77-3cdf8c1896a7","year":2022},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.226133Z"},"links":{"citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:5d4ea8749e8daaa37f3240e2421802e442db26e1ed58a8923116f73187798daf","observation_id":"fd4b86e0-3e56-4216-a88e-b9b7ded4fe4c","resolution":{"observed_at":"2026-08-15T23:34:31.059239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13373","last_updated":"2024-09-20T10:20:46Z","snapshot_observed_at":"2026-08-16T13:16:55.432324Z","submitted_at":"2024-09-20T10:20:46Z","title":"LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13373","snapshot_observed_at":"2026-08-15T23:34:30.231316Z","title":"LLMs still can’t plan; Can lrms? A preliminary evaluation of OpenAI’s o1 on planbench","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.231316Z"},"links":{"cited_paper":"/paper/2409.13373","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:be60a302d6eeba1144c876b3219ab96bc22eb5bec81683a828f148818a906c32","observation_id":"670b3644-a336-4a03-ac17-3bc7b22a679e","resolution":{"observed_at":"2026-08-15T23:34:30.231316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16269","last_updated":"2024-02-26T03:10:11Z","snapshot_observed_at":"2026-08-16T14:15:27.914836Z","submitted_at":"2024-02-26T03:10:11Z","title":"From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16269","snapshot_observed_at":"2026-08-15T23:34:30.240422Z","title":"d., Mirzazadeh, F., Birbil, I., Kurtz, J., and Maragno, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.240422Z"},"links":{"cited_paper":"/paper/2402.16269","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:b69d36407a01bb0c2a01e7d54adde2640e5bc4cc08b5102f6a0c1514b0feb8dc","observation_id":"8b073c0f-ae6f-4f52-965c-3d3a3ef60c2a","resolution":{"observed_at":"2026-08-15T23:34:30.240422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.06209","last_updated":"2017-04-20T16:15:40Z","snapshot_observed_at":"2026-08-16T08:39:14.978501Z","submitted_at":"2017-04-20T16:15:40Z","title":"ADMM Penalty Parameter Selection by Residual Balancing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.06209","snapshot_observed_at":"2026-08-15T23:34:30.244880Z","title":"ADMM penalty parameter selection by residual balancing","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.244880Z"},"links":{"cited_paper":"/paper/1704.06209","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:5f1298aff7db40b16e5db373674916881dcea75515824955fed89905ee4e278b","observation_id":"91fb4199-b9e0-49c5-80d3-d2cdbdc57850","resolution":{"observed_at":"2026-08-15T23:34:30.244880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07864","last_updated":"2023-09-19T08:29:18Z","snapshot_observed_at":"2026-08-17T06:15:32.510873Z","submitted_at":"2023-09-14T17:12:03Z","title":"The Rise and Potential of Large Language Model Based Agents: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07864","snapshot_observed_at":"2026-08-15T23:34:30.253861Z","title":"The rise and potential of large language model based agents: A survey","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.253861Z"},"links":{"cited_paper":"/paper/2309.07864","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:e357458c9524aa16a33be4feac8c69566a975940b1a506980f40969a45841de2","observation_id":"3c170d17-5e22-4190-ac29-a28e17f1f8e3","resolution":{"observed_at":"2026-08-15T23:34:30.253861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.09686","last_updated":"2025-01-23T08:44:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T17:37:58Z","title":"Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.09686","snapshot_observed_at":"2026-08-15T23:34:30.258230Z","title":"Towards large reasoning models: A survey of reinforced reasoning with large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.258230Z"},"links":{"cited_paper":"/paper/2501.09686","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:c97e7513297fd871372e618ffa5bce621c6653fdc40f60a2c7e27eff53de579e","observation_id":"6cf60be3-7669-4d89-8e1d-fcb241441ddd","resolution":{"observed_at":"2026-08-15T23:34:30.258230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13116","last_updated":"2024-10-21T16:22:33Z","snapshot_observed_at":"2026-08-10T17:26:33.432994Z","submitted_at":"2024-02-20T16:17:37Z","title":"A Survey on Knowledge Distillation of Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13116","snapshot_observed_at":"2026-08-15T23:34:30.262683Z","title":"A survey on knowledge distillation of large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.262683Z"},"links":{"cited_paper":"/paper/2402.13116","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:be62336739a4cd5f674664de87ea658a167b005f4de98be5673524c5c5bcfb4f","observation_id":"ec3770ee-c4da-49da-9eb8-6733631cad7e","resolution":{"observed_at":"2026-08-15T23:34:30.262683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02543","last_updated":"2024-07-17T15:55:51Z","snapshot_observed_at":"2026-08-16T13:46:10.092559Z","submitted_at":"2024-06-04T17:58:18Z","title":"To Believe or Not to Believe Your LLM","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02543","snapshot_observed_at":"2026-08-15T23:34:30.267699Z","title":"Admm without a fixed penalty parameter: Faster convergence with new adaptive penalization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.267699Z"},"links":{"cited_paper":"/paper/2406.02543","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:2e98b891dbaa9d72e26c7bd126f62d6d9119bb296ec82b731bb2ad5a10d3fd20","observation_id":"44e3a969-e342-4858-9a0d-275dfab7d7a5","resolution":{"observed_at":"2026-08-15T23:34:30.267699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.03700","last_updated":"2022-08-23T11:19:06Z","snapshot_observed_at":"2026-08-16T16:40:42.385583Z","submitted_at":"2022-08-07T11:28:50Z","title":"A Survey of ADMM Variants for Distributed Optimization: Problems, Algorithms and Features","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.03700","snapshot_observed_at":"2026-08-15T23:34:30.272174Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.272174Z"},"links":{"cited_paper":"/paper/2208.03700","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:a31ee1933d76d84f748825b2ffaa20c6576afd8b80be5df313eccad035ddd11f","observation_id":"9f6d3df2-8de5-4771-b2aa-b682e6bbb8c7","resolution":{"observed_at":"2026-08-15T23:34:30.272174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.13306","last_updated":"2024-02-27T14:59:46Z","snapshot_observed_at":"2026-08-14T18:06:23.925586Z","submitted_at":"2018-10-31T14:35:38Z","title":"Automated Machine Learning: From Principles to Practices","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.13306","snapshot_observed_at":"2026-08-15T23:34:30.276798Z","title":"Taking human out of learning applications: A survey on automated machine learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.276798Z"},"links":{"cited_paper":"/paper/1810.13306","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:285ddd75ca2aac4f1e67ef6c6ca357e4e4864c0cc95f32aeb7c292b1c0d8bfbc","observation_id":"2fbe7bcd-a4a9-4948-b132-1cbe5cd331ba","resolution":{"observed_at":"2026-08-15T23:34:30.276798Z","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":"2026-08-15T23:34:30.281215Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.281215Z"},"links":{"cited_paper":"/paper/2210.03629","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:31f4372a1cfafa4dd9944e648bbdc5548f297a85af18bff00659beec3ad1ddd1","observation_id":"ff047553-0c7e-41d2-9f00-c6c9279846d8","resolution":{"observed_at":"2026-08-15T23:34:30.281215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.16867","last_updated":"2025-02-04T05:06:39Z","snapshot_observed_at":"2026-08-16T13:15:31.442603Z","submitted_at":"2024-09-25T12:32:41Z","title":"Multi-objective Evolution of Heuristic Using Large Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.16867","snapshot_observed_at":"2026-08-15T23:34:30.285773Z","title":"Multi-objective evolution of heuristic using large language model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.285773Z"},"links":{"cited_paper":"/paper/2409.16867","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:5818e89e7956a8b9718e844ad9f7dbef0e2d2934b9e384d0e85a04222718a1dd","observation_id":"445fa47e-0597-45a8-b22f-ea90a0895082","resolution":{"observed_at":"2026-08-15T23:34:30.285773Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:34:31.037378Z","title":"Using large language models for hyperparameter optimization","venue":null,"work_id":"6a5cfa71-b8fe-405e-b00e-7e55cc76e9d8","year":2023},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.290087Z"},"links":{"citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:d4c26c5106fb4a55f931f2bed72a90938fb1b2b283f6a25b02439a7f562959f6","observation_id":"5299d5b8-79cd-42a3-b758-3862115c134d","resolution":{"observed_at":"2026-08-15T23:34:31.043660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02499","last_updated":"2023-05-04T02:09:43Z","snapshot_observed_at":"2026-08-16T15:35:48.089812Z","submitted_at":"2023-05-04T02:09:43Z","title":"AutoML-GPT: Automatic Machine Learning with GPT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02499","snapshot_observed_at":"2026-08-15T23:34:30.294228Z","title":"AutoML-GPT: Automatic machine learning with GPT","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.294228Z"},"links":{"cited_paper":"/paper/2305.02499","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:0d2e0863104d0677a0fd6ee6464ed1e6771db743755c2d6c97eb5e2fec6adcc2","observation_id":"9380d3d0-131e-4fbf-9b9c-0defbe1ec0ed","resolution":{"observed_at":"2026-08-15T23:34:30.294228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04643","last_updated":"2023-02-09T13:57:06Z","snapshot_observed_at":"2026-08-16T20:11:13.523430Z","submitted_at":"2023-02-09T13:57:06Z","title":"A Novel Approach for Auto-Formulation of Optimization Problems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04643","snapshot_observed_at":"2026-08-15T23:34:30.179632Z","title":"A novel approach for auto-formulation of optimization problems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":1965,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.179632Z"},"links":{"cited_paper":"/paper/2302.04643","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:383f2cd7f66d7775b5f82db160dd86d8b43bced9f2a2ee9c9d982b9144725d38","observation_id":"b293e6fe-8a5a-4539-9696-5443fd115503","resolution":{"observed_at":"2026-08-15T23:34:30.179632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10034","last_updated":"2024-05-29T09:00:25Z","snapshot_observed_at":"2026-08-16T14:26:26.276254Z","submitted_at":"2024-01-18T14:58:17Z","title":"Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10034","snapshot_observed_at":"2026-08-15T23:34:30.249473Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":1997,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.249473Z"},"links":{"cited_paper":"/paper/2401.10034","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:93c00c9c7eb92bc694f2fcf5b8699f1cbbe1b4f55ed8d4204db5570286755e41","observation_id":"14791a53-fadc-4c5c-9b4a-4c1fa860f241","resolution":{"observed_at":"2026-08-15T23:34:30.249473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11050","last_updated":"2024-10-04T04:40:03Z","snapshot_observed_at":"2026-08-18T02:21:46.521643Z","submitted_at":"2024-06-16T19:22:53Z","title":"A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11050","snapshot_observed_at":"2026-08-15T23:34:30.126436Z","title":"J., Taylor, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":1999,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.126436Z"},"links":{"cited_paper":"/paper/2406.11050","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:f18fe588d586c10a2bde5033544be87c7294030075c50738aff8ecee1cb817ae","observation_id":"7c883ce0-5b93-44d9-a100-db29c2dfd5d6","resolution":{"observed_at":"2026-08-15T23:34:30.126436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10478","last_updated":"2024-12-25T16:38:51Z","snapshot_observed_at":"2026-08-16T13:01:00.375260Z","submitted_at":"2024-11-11T21:54:26Z","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10478","snapshot_observed_at":"2026-08-15T23:34:30.104946Z","title":"Large language models for constructing and optimizing machine learning workflows: A survey","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2000,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.104946Z"},"links":{"cited_paper":"/paper/2411.10478","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:efcbd9935946bdbef44538ac973dcaadc303bff3c8098d43e58d3a560b3556f8","observation_id":"30d48564-a99e-4528-bcde-ef0b067729c9","resolution":{"observed_at":"2026-08-15T23:34:30.104946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08073","last_updated":"2022-12-15T06:19:23Z","snapshot_observed_at":"2026-08-16T03:49:00.703994Z","submitted_at":"2022-12-15T06:19:23Z","title":"Constitutional AI: Harmlessness from AI Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08073","snapshot_observed_at":"2026-08-15T23:34:30.057292Z","title":"Constitutional ai: Harmlessness from ai feedback","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.057292Z"},"links":{"cited_paper":"/paper/2212.08073","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:c33389f0b8915e458273c904e66fd110b4f5f7bd7c41f21043e43036889e4a59","observation_id":"0c38b30d-4cb0-4b14-b2e1-e8bbc7a4c1fa","resolution":{"observed_at":"2026-08-15T23:34:30.057292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20011","last_updated":"2024-10-25T23:52:28Z","snapshot_observed_at":"2026-08-16T13:05:49.624956Z","submitted_at":"2024-10-25T23:52:28Z","title":"A Survey of Small Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20011","snapshot_observed_at":"2026-08-15T23:34:30.236086Z","title":"A survey of small language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.236086Z"},"links":{"cited_paper":"/paper/2410.20011","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:2686d9481d8ffe78d5b42e157352a153c9ffc46aa410fae5d78c509af59ae276","observation_id":"62b946f3-18a8-436e-9ec7-96975628867b","resolution":{"observed_at":"2026-08-15T23:34:30.236086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.11074","last_updated":"2025-05-27T07:23:00Z","snapshot_observed_at":"2026-08-17T16:59:44.988324Z","submitted_at":"2025-03-14T04:34:31Z","title":"Exploring the Necessity of Reasoning in LLM-based Agent Scenarios","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.11074","snapshot_observed_at":"2026-08-15T23:34:30.298591Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.298591Z"},"links":{"cited_paper":"/paper/2503.11074","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:ce6133009dca181f2ca1029387f9d786e769e41dd9bedf4f25737ba3cdba1f16","observation_id":"7d112a31-3361-4fac-abad-6c778b79ac56","resolution":{"observed_at":"2026-08-15T23:34:30.298591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.12534","last_updated":"2025-05-11T09:58:48Z","snapshot_observed_at":"2026-08-16T13:59:36.664469Z","submitted_at":"2024-04-18T22:54:08Z","title":"Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.12534","snapshot_observed_at":"2026-08-15T23:34:30.207713Z","title":"Towards large language models as copilots for theorem proving in lean","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.207713Z"},"links":{"cited_paper":"/paper/2404.12534","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:1be8acb37c767c0cd6e3d4cc89d9367250174e9629121ef1bfbfd281ed222ba9","observation_id":"f00b8801-cb29-43f6-a244-03fa13f682f9","resolution":{"observed_at":"2026-08-15T23:34:30.207713Z","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":"2026-08-16T14:53:40.987637Z","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":"2026-08-15T23:34:30.041775Z","title":"Optimus: Optimization modeling using mip solvers and large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.041775Z"},"links":{"cited_paper":"/paper/2310.06116","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:25a6b2b7a5eebbfe9001d33641fa14faf0a423ec39b294cc0e7dafa3c2a7f067","observation_id":"ef3a9a73-40c2-43da-a207-a5f7937b9171","resolution":{"observed_at":"2026-08-15T23:34:30.041775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18679","last_updated":"2024-10-15T15:52:57Z","snapshot_observed_at":"2026-08-16T14:14:23.095590Z","submitted_at":"2024-02-28T19:49:55Z","title":"Data Interpreter: An LLM Agent For Data Science","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18679","snapshot_observed_at":"2026-08-15T23:34:30.113359Z","title":"Data interpreter: An LLM agent for data science","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.113359Z"},"links":{"cited_paper":"/paper/2402.18679","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:5489ce1b8d2f3de4b0a73bb75fc4822ec9d704212b84718228e95b0a1fcde787","observation_id":"6c6daf2e-db15-404b-8b1f-97437d2aa9f3","resolution":{"observed_at":"2026-08-15T23:34:30.113359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.07118","last_updated":"2021-04-12T08:16:59Z","snapshot_observed_at":"2026-08-14T09:17:46.140640Z","submitted_at":"2020-09-15T14:18:53Z","title":"It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.07118","snapshot_observed_at":"2026-08-15T23:34:30.203124Z","title":"and Sch¨utze, H","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.203124Z"},"links":{"cited_paper":"/paper/2009.07118","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:f2d73b716d7ea45a2cca01c53b9ebd6500872f79beeb93deeb3ab2d970c569da","observation_id":"68b85adc-68de-43e9-b74e-76f847c66b3c","resolution":{"observed_at":"2026-08-15T23:34:30.203124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18137","last_updated":"2024-11-04T11:16:38Z","snapshot_observed_at":"2026-08-16T13:48:38.580844Z","submitted_at":"2024-05-28T12:51:01Z","title":"Exploiting LLM Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18137","snapshot_observed_at":"2026-08-15T23:34:30.091392Z","title":"Exploiting LLM quantization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.091392Z"},"links":{"cited_paper":"/paper/2405.18137","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:32a97431434f3f518efe564f27847da9fe899dacfb098fb6926782dacf20fe2c","observation_id":"43bf6354-c6b8-48c6-ab3a-61a5b7190fd2","resolution":{"observed_at":"2026-08-15T23:34:30.091392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01881","last_updated":"2025-02-26T13:57:13Z","snapshot_observed_at":"2026-08-16T14:22:00.658392Z","submitted_at":"2024-02-02T20:12:05Z","title":"Large Language Model Agent for Hyper-Parameter Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01881","snapshot_observed_at":"2026-08-15T23:34:30.157614Z","title":"Large language model agent for hyper-parameter optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.157614Z"},"links":{"cited_paper":"/paper/2402.01881","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:7b89fae878d0d9dbb02a7c46855715c91ce8b71d0d5a778e891ac04bc6855893","observation_id":"73617755-a0a7-4e0a-80a9-86dfd01944b5","resolution":{"observed_at":"2026-08-15T23:34:30.157614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.05811","last_updated":"2023-05-09T23:51:14Z","snapshot_observed_at":"2026-08-16T15:34:19.434853Z","submitted_at":"2023-05-09T23:51:14Z","title":"Towards an Automatic Optimisation Model Generator Assisted with Generative Pre-trained Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05811","snapshot_observed_at":"2026-08-15T23:34:30.047365Z","title":"Towards an automatic optimisation model generator assisted with generative pre-trained transformer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.047365Z"},"links":{"cited_paper":"/paper/2305.05811","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:01328df6d1e6f71692d3facd5a2c19eaba7fc2a4d7da65da0cbeba8369033721","observation_id":"429be844-d008-4c85-ba59-90bfed5c753b","resolution":{"observed_at":"2026-08-15T23:34:30.047365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05915","last_updated":"2023-10-09T17:58:38Z","snapshot_observed_at":"2026-08-16T14:53:43.494263Z","submitted_at":"2023-10-09T17:58:38Z","title":"FireAct: Toward Language Agent Fine-tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05915","snapshot_observed_at":"2026-08-15T23:34:30.071160Z","title":"Fireact: Toward language agent fine-tuning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.071160Z"},"links":{"cited_paper":"/paper/2310.05915","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:938fd5a19a15ba5eeb5def4a985938733eca873e3303fcbb5b28c30585661d39","observation_id":"edcfd5ca-01dc-421b-9e57-e1fe127e13f3","resolution":{"observed_at":"2026-08-15T23:34:30.071160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10098","last_updated":"2024-05-16T13:54:37Z","snapshot_observed_at":"2026-08-16T13:52:10.006578Z","submitted_at":"2024-05-16T13:54:37Z","title":"When Large Language Model Meets Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10098","snapshot_observed_at":"2026-08-15T23:34:30.117730Z","title":"When large language model meets optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.117730Z"},"links":{"cited_paper":"/paper/2405.10098","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:fa52b9beb5a5bbe6cfbec0ce7a443dbcdc366c17dc359238657347f302f62a09","observation_id":"a5393245-4e8f-406b-834b-9a9b1ec02bce","resolution":{"observed_at":"2026-08-15T23:34:30.117730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03488","last_updated":"2020-12-16T22:36:27Z","snapshot_observed_at":"2026-08-14T13:08:22.170216Z","submitted_at":"2020-07-07T14:20:26Z","title":"Benchmarking in Optimization: Best Practice and Open Issues","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03488","snapshot_observed_at":"2026-08-15T23:34:30.061937Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.061937Z"},"links":{"cited_paper":"/paper/2007.03488","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:bb382181b4c4b0c1b057d3be59a6aac5eeb9e163290061f0d37d5d828439f9c0","observation_id":"cd4417bd-cfac-49e1-8adb-b89e99dc0b0a","resolution":{"observed_at":"2026-08-15T23:34:30.061937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.13218","last_updated":"2025-04-22T02:13:17Z","snapshot_observed_at":"2026-08-16T14:58:18.325736Z","submitted_at":"2023-09-22T23:45:21Z","title":"Language Models for Business Optimisation with a Real World Case Study in Production Scheduling","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.13218","snapshot_observed_at":"2026-08-15T23:34:30.052278Z","title":"T., Nguyen, S., Sun, Y., and Alahakoon, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.052278Z"},"links":{"cited_paper":"/paper/2309.13218","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:5f07dba1f0ea66c0587fbfc3c2b3cc16391df7bf624f2069b051410ab55626cc","observation_id":"57224dda-392d-4e3f-b867-6edb8263df2f","resolution":{"observed_at":"2026-08-15T23:34:30.052278Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:34:31.100397Z","title":"and Mattingley, J","venue":null,"work_id":"f9afe30b-f907-48fb-9fdf-4900d1ffe560","year":2006},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.066825Z"},"links":{"citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:af8cf8d07e30bc678cedc4236de8bec1c5c3def7fc9ab6437e21d7597575e18e","observation_id":"be80cc88-dc7b-4d0f-88f1-4f5635317d63","resolution":{"observed_at":"2026-08-15T23:34:31.104922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.03428","last_updated":"2024-01-07T09:08:24Z","snapshot_observed_at":"2026-08-16T14:29:21.180056Z","submitted_at":"2024-01-07T09:08:24Z","title":"Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.03428","snapshot_observed_at":"2026-08-15T23:34:30.075992Z","title":"M., and Yu, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T23:34:30.075992Z"},"links":{"cited_paper":"/paper/2401.03428","citing_paper":"/paper/2505.04354"},"observation_digest":"sha256:b46d513edde663e3887f6ba25d7ce2bc5f180e75da5e53ae9ffffa822732872d","observation_id":"4fed519c-e23f-4e5c-abfe-eb7e68396a9c","resolution":{"observed_at":"2026-08-15T23:34:30.075992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.04354","last_updated":"2025-05-07T12:07:49Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-17T16:50:07.388948Z","submitted_at":"2025-05-07T12:07:49Z","title":"Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":57},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2505.04354."}