| [1] | Newell A., Shaw J.C. and Simon H.A. (1959). Report on a general problem-solving program. Proc. Int. Conf. Inf. Process., pp. 256–264. |
| [2] | Gleixner A., Hendel G., Gamrath G. et al. (2021). MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library. Math. Program. Comput. 13(3):443−490. DOI:10.1007/s12532-020-00194-3 |
| [3] | AhmadiTeshnizi A., Gao W. and Udell M. (2024). OptiMUS: Scalable optimization modeling with (MI)LP solvers and large language models. Proc. Mach. Learn. Res., pp. 577–596. |
| [4] | Jiang C., Shu X., Qian H. et al. (2025). LLMOPT: Learning to define and solve general optimization problems from scratch. Proc. Int. Conf. Learn. Represent. |
| [5] | Huang C., Tang Z., Hu S. et al. (2025). ORLM: A customizable framework in training large models for automated optimization modeling. Oper. Res. 73(6):2986−3009. DOI:10.1287/opre.2024.1233 |
| [6] | Romera-Paredes B., Barekatain M., Novikov A. et al. (2024). Mathematical discoveries from program search with large language models. Nature 625:468−475. DOI:10.1038/s41586-023-06924-6 |
| [7] | Tang K. and Yao X. (2024). Learn to optimize—a brief overview. Natl. Sci. Rev. 11:nwae132. DOI:10.1093/nsr/nwae132 |
| [8] | Moradi B., Muñoz M.A. and Kirley M. (2026). Beyond distribution shift: Investigating generalisation in automated algorithm selection for single-objective black-box optimisation. IEEE Trans. Evol. Computat., pp. 1–1. DOI:10.1109/TEVC.2026.3697890. |
| [9] | Wang Z., Liu S., Yang P. et al. (2025). Evolving generalizable parallel algorithm portfolios for binary optimization problems via domain-agnostic instance generation. IEEE Trans. Evol. Computat., pp. 1–1. DOI:10.1109/TEVC.2025.3635185. |
| [10] | Baratchi M., Wang C., Limmer S. et al. (2024). Automated machine learning: Past, present and future. Artif. Intell. Rev. 57:122. DOI:10.1007/s10462-024-10726-1 |
| Liu S. and Tang K. (2026). From general problem solver to scalable agentic problem solver. AI Plus 1:100009. https://doi.org/10.59717/ipj.aiplus.2026.100009 |
To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.
Illustration of APS and algorithm space distillation as its scaling layer.