Uncertainty-Aware Sequential Decision Rules for Event-Triggered LLM Invocation in Streaming Systems

Jul 16, 2026

Researchers formalize the problem of deciding when to invoke a large language model (LLM) in streaming inference pipelines as a risk-based sequential stopping problem. They provide theoretical guarantees, including sublinear regret and optimality of threshold policies, and empirically validate their approach on turbofan degradation data with real LLM calls. The study finds that anomaly-score-driven risk functions outperform several baselines by a significant margin on Pareto AUC.

Why it matters: This work introduces a principled and theoretically grounded framework for cost-effective LLM invocation in streaming systems, with demonstrated empirical benefits that could reduce inference costs in real-time applications.

Full story at: arXiv AI/ML ↗