Title: PRISM: Festina Lente Proactivity—Risk-Sensitive, Uncertainty-Aware Deliberation for Proactive Agents
Primary area: foundation or frontier models, including LLMs
Keywords: Agent

Abstract: Proactive agents must decide not only what to say but, critically, when to intervene. We frame this challenge as cost-sensitive selective intervention and introduce PRISM, a framework that weds a decision-theoretic gate with a dual-process reasoning architecture. At inference, an intervention is triggered only when the calibrated probability of user acceptance surpasses a threshold derived from the asymmetric costs of misses versus false alarms. A resource-intensive 'Slow' deliberation, featuring counterfactual reasoning, is selectively invoked for borderline cases, concentrating finite computational resources on the most ambiguous and high-stakes decisions. The system is trained via gate-aligned, schema-locked distillation, where a teacher executing the full PRISM pipeline provides dense, executable supervision on unlabeled interaction data. The student’s response policy is explicitly decoupled from the intervention gate, enabling tunable, auditable behavior. Evaluated on ProactiveBench and in-the-wild traces, PRISM significantly reduces false alarms by 22.78% and improves F1-score by 20.14% over strong baselines. Ultimately, PRISM offers a principled and performant framework for building proactive agents that are precise, controllable, and trustworthy.