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.

LAWS represents a significant advancement in making neural network inference more efficient and verifiable for deployment scenarios where computational resources are constrained. The core innovation lies in its self-certification capability—the system can mathematically guarantee approximation error bounds at runtime, eliminating the need for ground truth comparisons during deployment. This addresses a critical pain point in edge AI where validators may be unavailable or too expensive to run continuously.

The architecture's theoretical contributions extend beyond immediate practical applications. By proving that LAWS generalizes both Mixture-of-Experts and KV prefix caching as special cases, the researchers establish a unifying framework for understanding different inference optimization strategies. The monotone hit rate theorem and growth rate analysis provide deployment engineers with predictable scaling characteristics, enabling better resource planning. The O(2^H log N) expert library growth bound ties performance directly to workload entropy, offering concrete guidance on system dimensioning.

For the AI industry, this work addresses a growing tension between model capability and deployment feasibility. As models become larger, inference bottlenecks increasingly limit their practical utility. LAWS enables organizations to run sophisticated models on edge devices and robotics platforms with formal guarantees on output quality. The fleet learning convergence theorem with Omega(K) speedup suggests potential for distributed inference systems, particularly valuable for multi-agent robotics applications.

The over-the-air update bandwidth constraints make the approach particularly relevant for IoT and edge deployments where connectivity is limited. Future validation of the conjectured acquisition-optimality and polynomial Lipschitz growth could establish LAWS as the definitive standard for production inference caching.

• →LAWS provides mathematically verifiable error bounds for cached neural inference without requiring ground truth at deployment time
• →The framework unifies Mixture-of-Experts and KV prefix caching as special cases while offering greater expressiveness than either approach
• →Expert library growth scales as O(2^H log N) based on workload entropy, enabling predictable resource planning for edge deployments
• →Fleet-based learning achieves Omega(K) speedup for K-unit systems, supporting distributed inference for robotics and multi-agent applications
• →Self-certification eliminates expensive validation requirements, making deployment more feasible for resource-constrained environments