A parameter-free regularized Newton-type algorithm that simultaneously achieves optimal global complexity and quadratic local convergence in nonconvex optimization.

AdaPA-Agent enhances LLM agent personalization by dynamically modeling user preference strengths via 'Adaptive Preference Arithmetic,' learning from existing interactions without extra user feedback.