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This work introduces Stackelberg Program Optimization (SPO), an LLM-based framework for discovering adaptive executable destroy-repair programs that outperforms strong baselines across a broad range of settings and generalizes beyond the discovery scale to larger instances and benchmark sets.
An interpretable and high-performing artificial intelligence framework for quantitative modeling of PDAC was established, and Objective characterization of tumor heterogeneity and accurate postoperative survival prediction are enabled, with potential value for personalized management in PDAC.
AutoPref is presented, the first LLM-guided framework for automated preference-objective discovery in NCO, which consistently outperforms strong hand-designed baselines across problem scales, demonstrating the benefits and scalability of automated objective discovery for NCO.
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