Establishes the first rigorous framework for continuous semantic caching of LLM responses using ε-net discretization and kernel ridge regression, with sublinear regret bounds.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Variability modeling from software engineering enables systematic sampling, measurement, and prediction of LLM inference configurations for energy, latency, and accuracy trade-offs.
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
citing papers explorer
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Continuous Semantic Caching for Low-Cost LLM Serving
Establishes the first rigorous framework for continuous semantic caching of LLM responses using ε-net discretization and kernel ridge regression, with sublinear regret bounds.
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Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
Variability modeling from software engineering enables systematic sampling, measurement, and prediction of LLM inference configurations for energy, latency, and accuracy trade-offs.
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From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.