2025/02/03 by Neel Kant, Kant, Neel · 2 voices · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Assembly Line Balancing Optimization #FOS: Computer and information sciences #Manufacturing Process and Optimization #Scheduling and Optimization Algorithms #cs.AI
paper · pdf · doi:10.48550/arxiv.2502.01492
openalex publication_date 2025/02/03 · arxiv published 2025/02/03 · arxiv updated 2025/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Continuing advances in frontier model research are paving the way for widespread deployment of AI agents. Meanwhile, global interest in building large, complex systems in software, manufacturing, energy and logistics has never been greater. Although AI driven system engineering holds tremendous promise, the static benchmarks dominating agent evaluations today fail to capture the crucial skills required for implementing dynamic systems, such as managing uncertain trade-offs and ensuring proactive adaptability. This position paper advocates for training and evaluating AI agents' system engineering abilities through automation-oriented sandbox games-particularly Factorio. By directing research efforts in this direction, we can equip AI agents with the specialized reasoning and long-horizon planning necessary to design, maintain, and optimize tomorrow's most demanding engineering projects.