# Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting

arXiv: https://arxiv.org/abs/2602.08403 · HTML: https://mlsanity.com/paper/2602.08403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://mlsanity.com/paper/2006.05990/) (2020)
- [TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection](https://mlsanity.com/paper/1908.05786/) (2019)
- [On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives](https://mlsanity.com/paper/2404.04059/) (2024)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning](https://mlsanity.com/paper/2404.10163/) (2024)
- [Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction](https://mlsanity.com/paper/2404.14232/) (2024)
- [Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation](https://mlsanity.com/paper/2007.13090/) (2020)
- [Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces](https://mlsanity.com/paper/2405.09255/) (2024)
- [Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights](https://mlsanity.com/paper/2405.09695/) (2024)
- [The Design of Informative Take-Over Requests for Semi-Autonomous Cyber-Physical Systems: Combining Spoken Language and Visual Icons in a Drone-Controller Setting](https://mlsanity.com/paper/2409.08253/) (2024)
- [Understanding and Predicting Temporal Visual Attention Influenced by Dynamic Highlights in Monitoring Task](https://mlsanity.com/paper/2510.08777/) (2025)

## Abstract (arXiv)

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 behavior to simulate attentional dynamics during monitoring. Using a delivery-drone oversight scenario, we present initial results suggesting that RL-based highlighting can outperform static, rule-based approaches and discuss challenges of intelligent oversight support.