LAWS is a self-certifying parametrized cache that generalizes mixture-of-experts and KV caching with uniform error bounds based on Lipschitz constants and embedding diameters.
Sequential KV cache compression via probabilistic language tries: Beyond the per-vector Shannon limit
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2026 1verdicts
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LAWS: Learning from Actual Workloads Symbolically -- A Self-Certifying Parametrized Cache Architecture for Neural Inference, Robotics, and Edge Deployment
LAWS is a self-certifying parametrized cache that generalizes mixture-of-experts and KV caching with uniform error bounds based on Lipschitz constants and embedding diameters.