[2603.04378] Robustness of Agentic AI Systems via Adversarially-Aligned Jacobian Regularization

Computer Science > Machine Learning arXiv:2603.04378 (cs) [Submitted on 4 Mar 2026] Title:Robustness of Agentic AI Systems via Adversarially-Aligned Jacobian Regularization Authors:Furkan Mumcu, Yasin Yilmaz View a PDF of the paper titled Robustness of Agentic AI Systems via Adversarially-Aligned Jacobian Regularization, by Furkan Mumcu and 1 other authors View PDF HTML (experimental) Abstract:As Large Language Models (LLMs) transition into autonomous multi-agent ecosystems, robust minimax training becomes essential yet remains prone to instability when highly non-linear policies induce extreme local curvature in the inner maximization. Standard remedies that enforce global Jacobian bounds are overly conservative, suppressing sensitivity in all directions and inducing a large Price of Robustness. We introduce Adversarially-Aligned Jacobian Regularization (AAJR), a trajectory-aligned approach that controls sensitivity strictly along adversarial ascent directions. We prove that AAJR yields a strictly larger admissible policy class than global constraints under mild conditions, implying a weakly smaller approximation gap and reduced nominal performance degradation. Furthermore, we derive step-size conditions under which AAJR controls effective smoothness along optimization trajectories and ensures inner-loop stability. These results provide a structural theory for agentic robustness that decouples minimax stability from global expressivity restrictions. Subjects: Machine Lear...