Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills execute without giving the agent step-level guidance for adaptation or recovery. We introduce WebXSkill, a framework that bridges this gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance. WebXSkill operates in three stages: skill extraction mines reusable action subsequences from readily available synthetic agent trajectories and abstracts them into parameterized skills, skill organization indexes them into a URL-based graph for context-aware retrieval, and skill deployment exposes two complementary modes, grounded mode for fully automated execution and guided mode where skills serve as step-by-step instructions the agent follows with its native planning. WebXSkill demonstrates consistent improvements on WebArena, WebVoyager, and Online-Mind2Web. We further find that better skill deployment mode depends on a model’s plan and execution capability. The code is available at https://github.com/aiming-lab/WebXSkill.

With reasoning becoming the generative paradigm for large language models, the memory bottleneck caused by KV cache during the inference phase has become a critical factor limiting high-concurrency service capabilities. Although existing KV cache eviction methods address the memory issue, most of them are impractical for industrial-grade applications. This paper introduces Compressed PagedAttention, a method that combines token-wise KV cache eviction with PagedAttention. We propose a comprehensive scheduling strategy and support prefix caching and asynchronous compression for Compressed PagedAttention. Based on this, we have developed a high-concurrency inference engine, Zipage. On large-scale mathematical reasoning tasks, Zipage achieves around 95% of the performance of Full KV inference engines while delivering over 2.1 speedup.

We introduce GUI-360°, a large-scale dataset and benchmark suite for desktop productivity computer-using agents (CUAs). GUI-360° provides an automated pipeline for query sourcing, template-based environment construction, task instantiation, batched execution, and LLM-assisted quality filtering in Windows office applications. The corpus contains 131K validated successful action steps across 17K trajectories for training and evaluation, together with 1.09M failed execution steps for failure analysis and future recovery training. Each step provides screenshots, accessibility metadata, instantiated goals, reasoning traces, and structured GUI/API actions. GUI-360° supports GUI grounding, screen parsing, and action prediction under a hybrid GUI+API action space. Experiments show that current vision–language models struggle with fine-grained grounding and stepwise action prediction, while supervised fine-tuning on GUI-360° brings consistent in-domain and external benchmark gains. Our code and dataset are available at https://github.com/2020-qqtcg/GUI-360 and https://huggingface.co/datasets/vyokky/GUI-360.

We introduce UFO, a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications by observing and analyzing the GUI and control information of these applications. UFO utilizes a hierarchical dual-agent framework that decomposes user requests using a divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. It also incorporates a control interaction module tailored for Windows OS, which detects control elements effectively and allows for fully automated execution. As a result, UFO simplifies complex and time-consuming processes into tasks that can be completed with natural language commands.We conducted testing of UFO across 9 popular Windows applications, encompassing a variety of scenarios. The results derived from both quantitative metrics and real-case studies, underscore the superior effectiveness of UFOin fulfilling user requests. To the best of our knowledge, UFO stands as the first UI agent specifically tailored for task completion within the Windows OS.

This paper introduce a novel thought prompting approach called ”Everything of Thoughts” (XoT) for Large Language Models (LLMs) to defy the law of ”Penrose triangle” of existing thought paradigms, to achieve three key perspectives in thought generation simultaneously: performance, efficiency, and flexibility. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge and planning capability into thoughts, thereby enhancing LLMs’ decision-making capabilities. Through the MCTS-LLM collaborative thought revision framework, XoT autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to utilize flexible cognitive mappings for solving problems with multiple solutions.We evaluate XoT on several challenging problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches in various dimensions, showcasing its remarkable proficiency in addressing complex problems across diverse domains. The data and code are available at https://github.com/microsoft/Everything-of-Thoughts-XoT.