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Chuyue Tian, Jingfang Chen, Jianmai Shi
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10.1016/j.matpr.2022.04.366
10.1016/j.matpr.2022.04.366
10.1109/tqe.2022.3187367
10.1109/tqe.2022.3187367
10.1016/j.ejor.2020.07.063
10.1016/j.ejor.2020.07.063
DRoC: Elevating large language models for complex vehicle routing via decomposed retrieval of constraints
Kept as external metadata until matched
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Kept as external metadata until matched
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10.52202/075280-1345
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10.1007/s11425-023-2364-2
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10.18653/v1/2024.naacl-industry.42
10.1080/03155986.2024.2381306
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10.1609/aaai.v40i1.36983
10.1609/aaai.v40i1.36983
NL4Opt competition: Formulating optimization problems based on their natural language descriptions
Kept as external metadata until matched
Synthesizing mixed-integer linear programming models from natural language descriptions
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Kept as external metadata until matched
10.18653/v1/2025.emnlp-main.973
10.18653/v1/2025.emnlp-main.973
OptiMUS-0.3: Using large language models to model and solve optimization problems at scale
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Kept as external metadata until matched
Chain-of-Experts: When LLMs meet complex operations research problems
Kept as external metadata until matched
10.3390/info16020128
10.3390/info16020128
OR-LLM-Agent: Automating modeling and solving of operations research optimization problems with reasoning LLM
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Constraint modelling with LLMs using in-context learning
Kept as external metadata until matched
10.1109/iros60139.2025.11246517
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GraphArena: Evaluating and exploring large language models on graph computation
Kept as external metadata until matched
10.52202/085713-3539
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StepORLM: A self-evolving framework with generative process supervision for operations research language models
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10.1109/wcnc61545.2025.10978733
10.1109/wcnc61545.2025.10978733 · doi-reference
10.1109/lwc.2024.3462556
10.1109/lwc.2024.3462556 · doi-reference
10.1109/lwc.2025.3529082
10.1109/lwc.2025.3529082 · doi-reference
10.1109/tmc.2025.3637120
10.1109/tmc.2025.3637120 · doi-reference
10.1109/case59546.2024.10711695
10.1109/case59546.2024.10711695 · doi-reference
10.1186/s13677-025-00822-0
10.1186/s13677-025-00822-0 · doi-reference
10.3390/a17120582
10.3390/a17120582 · doi-reference
10.1109/mlcad65511.2025.11189204
10.1109/mlcad65511.2025.11189204 · doi-reference
10.1109/gcwkshps68340.2025.11591140
10.1109/gcwkshps68340.2025.11591140 · doi-reference
10.1109/lra.2024.3518105
10.1109/lra.2024.3518105 · doi-reference
10.52202/079017-4150
10.52202/079017-4150 · doi-reference
10.1109/mcii64973.2025.11032980
10.1109/mcii64973.2025.11032980 · doi-reference
10.1017/pds.2025.10234
10.1017/pds.2025.10234 · doi-reference
10.3390/ai6040066
10.3390/ai6040066 · doi-reference
10.1109/cec65147.2025.11043021
10.1109/cec65147.2025.11043021 · doi-reference
10.1109/tevc.2025.3561001
10.1109/tevc.2025.3561001 · doi-reference
10.1609/aaai.v39i25.34898
10.1609/aaai.v39i25.34898 · doi-reference
10.1109/tevc.2025.3556186
10.1109/tevc.2025.3556186 · doi-reference
10.1109/tevc.2024.3497793
10.1109/tevc.2024.3497793 · doi-reference
10.1145/3731567
10.1145/3731567 · doi-reference
10.1016/j.cor.2025.107325
10.1016/j.cor.2025.107325 · doi-reference
10.3390/app15158735
10.3390/app15158735 · doi-reference
10.1109/cec65147.2025.11043025
10.1109/cec65147.2025.11043025 · doi-reference
10.1038/s41586-023-06924-6
10.1038/s41586-023-06924-6 · doi-reference
10.1609/aaai.v40i43.41038
10.1609/aaai.v40i43.41038 · doi-reference
10.1016/j.eswa.2026.133022
10.1016/j.eswa.2026.133022 · doi-reference
10.1007/978-3-032-15635-8_23
10.1007/978-3-032-15635-8_23 · doi-reference
10.1109/tcad.2026.3652945
10.1109/tcad.2026.3652945 · doi-reference
10.1609/aaai.v40i43.41024
10.1609/aaai.v40i43.41024 · doi-reference
10.1109/cec60901.2024.10611913
10.1109/cec60901.2024.10611913 · doi-reference
10.1007/978-981-96-3538-2_13
10.1007/978-981-96-3538-2_13 · doi-reference
10.14428/esann/2024.es2024-209
10.14428/esann/2024.es2024-209 · doi-reference
10.1016/j.aei.2024.103042
10.1016/j.aei.2024.103042 · doi-reference
10.1609/aaai.v39i25.34922
10.1609/aaai.v39i25.34922 · doi-reference
10.52202/079017-1381
10.52202/079017-1381 · doi-reference
10.1287/opre.2024.1233
10.1287/opre.2024.1233 · doi-reference