Closing the Loop: Branch-and-Bound for Scalable Verification of Nonlinear Neural Feedback Systems

2026-09-14

AI Summary

该研究通过引入rail接口和clipper算法，利用分支定界法提高了非线性神经反馈系统验证的可扩展性。

Abstract

Despite recent advances in the verification of nonlinear neural feedback systems, scalability remains the central obstacle, as state-of-the-art solvers do not yet handle the network sizes and nonlinear dynamics of autonomy applications. Combinatorial solvers do not scale to large networks, whereas propagative solvers excessively sacrifice precision. This work seeks to improve the scalability of combinatorial solvers by formulating verification as branch-and-bound on an abstraction of the closed-loop system. We introduce \rail, an interface that exposes polyhedral enclosures of the dynamics to LiRPA-style bound propagation, and \clipper, a branch-and-bound algorithm that jointly refines enclosures and splits controller activations. This framework enables joint reasoning on the computational graph of the closed-loop system, preserving symbolic correlations across time steps. We present our construction and show that it yields significant improvements over the state of the art.

Problem

• nonlinear neural feedback systems
• scalability
• combinatorial solvers
• propagative solvers
• branch-and-bound

Innovation

• branch-and-bound
• nonlinear neural feedback systems
• scalability
• LiRPA-style bound propagation