Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

ArXi:2605.23320v1 Announce Type: new Ventilator decision requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit.