Derives the Pareto frontier for consistency C versus robustness R in randomized learning-augmented online bidding, with matching analytical bounds when R >= 2.885.
Competitive caching with machine learned advice
2 Pith papers cite this work. Polarity classification is still indexing.
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PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.
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The Pareto Frontier of Randomized Learning-Augmented Online Bidding
Derives the Pareto frontier for consistency C versus robustness R in randomized learning-augmented online bidding, with matching analytical bounds when R >= 2.885.
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Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management
PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.