Uncertainty-Aware Sequential Decision Rules for Event-Triggered LLM Invocation in Streaming Systems
Streaming inference pipelines increasingly pair lightweight fast models with Large Language Models (LLMs) that provide rich semantic understanding at substantial cost. The central question of when to invoke the LLM has received limited formal treatment. We cast this as a risk-based sequential stopping problem, where a trigger policy fires when a risk functional over the observation history exceeds a threshold. Within this framework, we prove six results: a minimum inter-event time bound excluding trigger chattering; optimality of threshold policies via smooth pasting; approximate SPRT guarantees under estimated parameters; O(sqrt(T log T)) regret for stationary streams, extending to O(sqr...
| Authors | Zhaohui Wang |
|---|---|
| Primary category | cs.LG |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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