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The paper introduces a branch-and-bound framework for scalable verification of nonlinear neural feedback systems, improving state-of-the-art methods by combining combinatorial and propagative solvers through tools like rail and clipper.
Loop engineering is the practice of designing systems where AI agents autonomously decide what to work on, execute, and iterate, going beyond manual prompting by building outer loops that compound across different domains. The article explains the two-layer agent harness and how sharing artifacts between loops creates compounding learning.
This paper presents six dimensions to characterize live feedback in interactive programming systems: granularity, reactivity, velocity, moldability, bidirectionality, and materiality, aiming to map the design space.