LAWS: Learning from Actual Workloads Symbolically -- A Self-Certifying Parametrized Cache Architecture for Neural Inference, Robotics, and Edge Deployment

Gregory Magarshak's new paper introduces LAWS (Learning from Actual Workloads Symbolically), a caching architecture for neural inference that self-certifies its accuracy. The core innovation is a library of 'expert functions' learned from actual deployment workloads, each covering a region of input space defined by a Probabilistic Language Trie (PLT). The central self-certification theorem gives a formal error bound: epsilon_fit + 2*Lambda(W)*C_E, where Lambda(W) is the model Lipschitz constant and C_E is the maximum embedding diameter. Crucially, this bound is checkable at deployment time without any ground truth labels, making it practical for real-world systems.

LAWS generalizes both Mixture-of-Experts (MoE) and KV prefix caching as special cases, and is strictly more expressive than fixed-K MoE or finite caches. The architecture includes a monotone hit rate theorem (coverage only increases with any-match routing) and an expert library growth rate of O(2^H log N) where H is workload entropy. For fleets of K edge units, LAWS achieves Omega(K) speedup via fleet learning convergence. The paper develops applications for LLM inference, robotic control, and multi-agent edge deployment, suggesting broad impact on efficient AI systems.