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 MLLMs suffer from two key issues: misinterpreting UI components and outdated knowledge.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.

Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However, existing dominant approaches, reward-guided search (RGS), suffer from a critical granularity mismatch: reward models (RMs) are trained on complete responses but applied to incomplete sequences during generation, leading to inconsistent scoring and suboptimal alignment. To combat the challenge, we argue that an ideal RM should satisfy two objectives: Score Consistency, ensuring coherent evaluation across partial and complete responses, and Preference Consistency, aligning partial sequence assessments with human preferences. To achieve these, we propose SPRM, a novel dual-consistency framework integrating score consistency-based and preference consistency-based partial evaluation modules, which leverage the Bradley-Terry model and entropy-based reweighting to predict cumulative rewards and prioritize human-aligned sequences. Extensive experiments on dialogue, summarization, and reasoning tasks demonstrate the effectiveness of SPRM, significantly reducing granularity discrepancies by up to 11.7 on TL;DR Summarization and achieving a 3.6%–10.3% improvement in GPT-4 evaluation scores across all tasks. Code is publicly available at [this link](https://github.com/xiebin23/SPRM).