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Sobolev Novelty is introduced, a term-level measure of structural independence for symbolic equations, which can be incorporated into diverse SR paradigms to support term pruning, search guidance, LLM feedback, and data selection, yielding consistent performance gains and demonstrating broad applicability.
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.
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.
Multi-Objective Tool-augmented Symbolic Regression (MOT-SR), a unified framework that integrates external analytical tools to extract structural priors and guide equation generation, while jointly optimizing for accuracy, complexity, and generalization via a multi-objective evaluation module that maintains a dynamic Pa...
EvoPINN is proposed, an agentic framework that reformulates PINN development from labor-intensive manual design into a rigorous, execution-grounded algorithm discovery problem, and autonomously invented SLRC-PINN, a novel architecture whose performance gains persist under rigorous parameter-matched comparisons.
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