Paper/Domain-Specific12 Agentic Reward Modeling: Verifying GUI Agent via Online Proactive Interaction Agentic Reward Modeling: Verifying GUI Agent via Online Proactive InteractionReinforcement learning with verifiable rewards (RLVR) is pivotal for the continuous evolution of GUI agents, yet existing evaluation paradigms face significant limitations. Rule-based methods suffer from poor scalability and cannot handle open-ended tasks,arxiv.org 2026. 3. 24. AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State Machines https://arxiv.org/abs/2602.14296 AutoWebWorld: Synthesizing Infinite Verifiable Web Environments via Finite State MachinesThe performance of autonomous Web GUI agents heavily relies on the quality and quantity of their training data. However, a fundamental bottleneck persists: collecting interaction trajectories from real-world websites is expensive and difficult to verify. Tarxiv.org 2026. 3. 10. UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action GenerationYuanzhang Lin, Zhe Zhang, He Rui, Qingao Dong, Mingyi Zhou, Jing Zhang, Xiang Gao, Hailong Sun. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.aclanthology.org 2026. 1. 28. The Evolution of Human-Like Computer-Using Agents From Perception to Command UFO: A UI-Focused Agent for Windows OS InteractionWe introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision. UFO employs a dual-agent framework to meticulously observe and analyze the graphical user interfacearxiv.org UFO2: The Desktop AgentOSRecent Computer-Using Agents (CUAs), powered by multimoda.. 2026. 1. 21. 이전 1 2 3 다음
