{"slug": "closing-the-loop-branch-and-bound-for-scalable-verification-of-nonlinear-neural", "title": "Closing the Loop: Branch-and-Bound for Scalable Verification of Nonlinear Neural Feedback Systems", "summary": "Researchers introduced RAIL, an interface exposing polyhedral enclosures of nonlinear dynamics to LiRPA-style bound propagation, and CLIPPER, a branch-and-bound algorithm that jointly refines enclosures and splits controller activations, to improve the scalability of combinatorial solvers for verifying nonlinear neural feedback systems. The work, published as arXiv:2609.16298v1, formulates verification as branch-and-bound on an abstraction of the closed-loop system, enabling joint reasoning on the closed-loop computational graph that preserves symbolic correlations across time steps. The authors report the framework yields significant improvements over the state of the art, addressing the scalability gap that leaves current solvers unable to handle the network sizes and nonlinear dynamics of autonomy applications.", "body_md": "arXiv:2609.16298v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/closing-the-loop-branch-and-bound-for-scalable-verification-of-nonlinear-neural", "canonical_source": "https://arxiv.org/abs/2609.16298", "published_at": "2026-09-16 04:00:00+00:00", "updated_at": "2026-09-16 04:07:38.681791+00:00", "lang": "en", "topics": ["ai-safety", "machine-learning", "neural-networks", "ai-research"], "entities": ["RAIL", "CLIPPER", "LiRPA", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/closing-the-loop-branch-and-bound-for-scalable-verification-of-nonlinear-neural", "markdown": "https://wpnews.pro/news/closing-the-loop-branch-and-bound-for-scalable-verification-of-nonlinear-neural.md", "text": "https://wpnews.pro/news/closing-the-loop-branch-and-bound-for-scalable-verification-of-nonlinear-neural.txt", "jsonld": "https://wpnews.pro/news/closing-the-loop-branch-and-bound-for-scalable-verification-of-nonlinear-neural.jsonld"}}