LAWS introduces a self-certifying inference caching architecture that builds a library of certified expert functions from deployment observations to reduce LLM inference costs.

LAWS (Learning from Actual Workloads Symbolically) constructs a growing library of certified expert functions from deployment observations, where each expert covers a region of input space and carries a formal error bound checkable at deployment time without ground truth.

The system generalizes both Mixture-of-Experts and KV prefix caching as special cases and proves strictly more expressive than any fixed-K MoE or finite cache, with expert library growth rate of O(2^H log N) where H is workload entropy.

Applications are developed for LLM inference, robotic control, and multi-agent edge deployment.