GUI-Rise: Structured Reasoning and History Summarization for GUI Navigation

• URL: http://arxiv.org/abs/2510.27210v1
• Date: Fri, 31 Oct 2025 06:10:57 GMT
• Title: GUI-Rise: Structured Reasoning and History Summarization for GUI Navigation
• Authors: Tao Liu, Chongyu Wang, Rongjie Li, Yingchen Yu, Xuming He, Bai Song,
• Abstract summary: We present a reasoning-enhanced framework that integrates structured reasoning, action prediction, and history summarization.<n>This framework employs specialized rewards, including a history-aware objective, directly linking summary quality to subsequent action performance.
• Score: 25.824982644530326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Multimodal Large Language Models (MLLMs) have advanced GUI navigation agents, current approaches face limitations in cross-domain generalization and effective history utilization. We present a reasoning-enhanced framework that systematically integrates structured reasoning, action prediction, and history summarization. The structured reasoning component generates coherent Chain-of-Thought analyses combining progress estimation and decision reasoning, which inform both immediate action predictions and compact history summaries for future steps. Based on this framework, we train a GUI agent, \textbf{GUI-Rise}, through supervised fine-tuning on pseudo-labeled trajectories and reinforcement learning with Group Relative Policy Optimization (GRPO). This framework employs specialized rewards, including a history-aware objective, directly linking summary quality to subsequent action performance. Comprehensive evaluations on standard benchmarks demonstrate state-of-the-art results under identical training data conditions, with particularly strong performance in out-of-domain scenarios. These findings validate our framework's ability to maintain robust reasoning and generalization across diverse GUI navigation tasks. Code is available at https://leon022.github.io/GUI-Rise.

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