Establishes the first rigorous framework for continuous semantic caching of LLM responses using ε-net discretization and kernel ridge regression, with sublinear regret bounds.
Cost-effective online multi-llm selection with versatile reward models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
Develops a constrained bandit algorithm for online LLM selection under packing and covering constraints with time-varying demand, claiming sublinear regret and constraint violations versus an offline full-information benchmark.
citing papers explorer
-
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.
-
KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
-
Online LLM Selection via Constrained Bandits with Time-Varying Demand
Develops a constrained bandit algorithm for online LLM selection under packing and covering constraints with time-varying demand, claiming sublinear regret and constraint violations versus an offline full-information benchmark.