AIBullisharXiv – CS AI · 6h ago7/10
🧠
LAWS: Learning from Actual Workloads Symbolically -- A Self-Certifying Parametrized Cache Architecture for Neural Inference, Robotics, and Edge Deployment
Researchers introduce LAWS, a self-certifying caching architecture for neural inference that builds a library of expert functions with formal error bounds, enabling efficient deployment across LLMs, robotics, and edge devices. The system generalizes both Mixture-of-Experts and KV prefix caching while providing mathematically verifiable performance guarantees without requiring ground truth validation.