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This paper formalizes the problem of when to invoke LLMs in streaming inference systems as a risk-based sequential stopping problem. It proves theoretical guarantees and empirically validates the framework on turbofan degradation data.
This paper introduces a meta-learning framework for anytime-valid certified robustness that uses sequential E-processes to adaptively allocate compute, achieving a 20-fold reduction in sample complexity compared to traditional randomized smoothing while maintaining rigorous statistical guarantees.