Group-based reinforcement learning typically relies on coarse outcome-level signals, such as group accuracy or variance, for data selection or replay. Yet in multi-turn tool-use rollout groups, these signals miss structural differences between successful and failed trajectories: a failure may diverge into an unrelated tool chain or differ from a successful trajectory only at a local decision. We therefore introduce Group Structural Proximity (GSP), which measures failed-to-successful rollout alignment within a group using tool dependency graphs induced by tool calls and argument flow. A controlled diagnostic study with outcome-statistics-matched subsets further shows that GSP captures distinct learning dynamics: high-GSP data drives localized policy refinement, while low-GSP data induces broader policy movement and higher-entropy exploration. This positions GSP as an effective metric on the exploration–exploitation tradeoff. Building on this insight, we introduce Proximity-Guided Policy Optimization (PGPO), which considers replay buffers with high- and low- GSP under an Exploration-to-Refinement Schedule (ERS) for group-based training. On 𝜏²-Bench and ACEBench Agent, PGPO improves sample efficiency and downstream tool-use performance, demonstrating the benefit of structure-aware group comparison in multi-turn tool-use reinforcement learning.

While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users’ more complex implicit intents. In this work, we highlight Hierarchical Implicit Intent Alignment for Personalized GUI Agent (PersonalAlign), a new agent task that requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions and anticipate latent routines by user state for proactive assistance. To facilitate this study, we introduce AndroidIntent, a benchmark designed to evaluate agents’ ability in resolving vague instructions and providing proactive suggestions through reasoning over long-term user records. We annotated 775 user-specific preferences and 215 routines from 20k long-term records across different users for evaluation. Furthermore, we introduce Hierarchical Intent Memory Agent (HIM-Agent), which maintains a continuously updating personal memory and hierarchically organizes user preferences and routines for personalization. Finally, we evaluate a range of GUI agents on AndroidIntent, including GPT-5, Qwen3-VL, and UI-TARS, further results show that HIM-Agent significantly improves both execution and proactive performance by 15.7% and 7.3%.

GUI automation faces critical challenges in dynamic environments. MLLMs suffer from two key issues: misinterpreting UI components and outdated knowledge. Traditional fine-tuning methods are costly for app-specific knowledge updates. We propose GUI-explorer, a training-free GUI agent that incorporates two fundamental mechanisms: (1) Autonomous Exploration of Function-aware Trajectory. To comprehensively cover all application functionalities, we design a Function-aware Task Goal Generator that automatically constructs exploration goals by analyzing GUI structural information (e.g., screenshots and activity hierarchies). This enables systematic exploration to collect diverse trajectories. (2) Unsupervised Mining of Transition-aware Knowledge. To establish precise screen-operation logic, we develop a Transition-aware Knowledge Extractor that extracts effective screen-operation logic through unsupervised analysis the state transition of structured interaction triples (observation, action, outcome). This eliminates the need for human involvement in knowledge extraction. With a task success rate of 53.7% on SPA-Bench and 47.4% on AndroidWorld, GUI-explorer shows significant improvements over SOTA agents. It requires no parameter updates for new apps. GUI-explorer is open-sourced and publicly available at https://github.com/JiuTian-VL/GUI-explorer.