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math.OC2026
Nonmonotone Coderivative-Based Newton Methods for Nonsmooth Optimization with Machine Learning Applications
Sina Kazemdehbashi, Yanchao Liu, Boris S. Mordukhovich
Newton-type algorithms are among the most effective methods for solving optimization problems because of their rapid local convergence. However, extending these methods to nonsmoot…
cs.CL2026
AQuA: Recursively Self-Improving Quantitative Trading Research Agents
Jiacheng Guo, Suozhi Huang, Yunlong Gao +5
We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve the hypothes…