Automating UI Optimization through Multi-Agentic Reasoning

2026-02-13

Technology Category

• Humans and AI: Intelligent User Interfaces
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present AutoOptimization, a novel multi-objective optimization framework for adapting user interfaces. From a user's verbal preferences for changing a UI, our framework guides a prioritization-based Pareto frontier search over candidate layouts. It selects suitable objective functions for UI placement while simultaneously parameterizing them according to the user's instructions to define the optimization problem. A solver then generates a series of optimal UI layouts, which our framework validates against the user's instructions to adapt the UI with the final solution. Our approach thus overcomes the previous need for manual inspection of layouts and the use of population averages for objective parameters. We integrate multiple agents sequentially within our framework, enabling the system to leverage their reasoning capabilities to interpret user preferences, configure the optimization problem, and validate optimization outcomes.

Problem

• UI optimization
• user preferences
• multi-objective optimization
• Pareto frontier
• automated layout adaptation

Innovation

• multi-agent reasoning
• UI optimization
• Pareto frontier search
• preference-driven design
• automated layout generation