WebAgentlab

🔥 Finding the "ChatGPT Moment" for CUA! On April 19, WebAgentLab x @qingke Community presents the "ICLR 2026 CUA Workshop" livestream. We've gathered top pioneers from UWaterloo, HKU, Fudan, Alibaba, and Minimax to deep-dive into: 💻 Real-world deployment & multi-platform unification of GUI Agents 🚀 Autonomous continual learning in dynamic environments 🛠️ Breaking data dependency in agent infrastructure Great research belongs beyond PDFs and repos. Join us to witness the new era of AI taking over the keyboard and mouse! 🖱️ #CUA #GUIAgent #LLM #AI #ICLR2026

OSExpert: Computer-Use Agents Learning Professional Skills via Exploration The paper presents the OSExpert framework, which enhances computer-use agents’ performance and efficiency in professional environments by enabling them to autonomously acquire and compose skills through a GUI-based exploration algorithm, achieving significant improvements in task completion and generalization compared to human experts. Jiateng Liu, Zhenhailong Wang, Rushi Wang, Bingxuan Li, Jeonghwan Kim, Aditi Tiwari, Pengfei Yu, Denghui Zhang, Heng Ji University of Illinois Urbana-Champaign; Stevens Institute of Technology arxiv.org/pdf/2603.07978

PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents The paper introduces PIRA-Bench, a benchmark for evaluating proactive intent recommendation agents that autonomously anticipate user actions from visual inputs, and presents the Proactive Intent Recommendation Framework (PIRF) to enhance performance in complex, noisy environments. Yuxiang Chai, Shunye Tang, Han Xiao @HanXiao27253141 , Rui Liu, Hongsheng Li MMLab @ CUHK; Nankai University; Huawei Research arxiv.org/pdf/2603.08013

SlowBA: An efficiency backdoor attack towards VLM-based GUI agents The paper presents SlowBA, a novel backdoor attack on vision-language model GUI agents that manipulates response latency through a two-stage reward-level backdoor injection strategy, revealing significant security vulnerabilities in response efficiency while maintaining task accuracy. Junxian Li, Tu Lan, Haozhen Tan, Yan Meng, Haojin Zhu Shanghai Jiao Tong University arxiv.org/pdf/2603.08316

SecAgent: Efficient Mobile GUI Agent with Semantic Context SecAgent is a multilingual mobile GUI agent that enhances smartphone automation by utilizing a novel semantic context mechanism and a large-scale Chinese dataset, achieving superior performance while addressing the limitations of existing models in non-English environments. Yiping Xie, Song Chen, Jingxuan Xing, Wei Jiang, Zekun Zhu, Yingyao Wang, Pi Bu, Jun Song, Yuning Jiang, Bo Zheng Taobao & Tmall Group of Alibaba arxiv.org/pdf/2603.08533

OpenClaw-RL: Train Any Agent Simply by Talking OpenClaw-RL is a novel framework that enhances agent performance by leveraging next-state signals from interactions for real-time reinforcement learning, enabling continuous learning and adaptation across various agentic settings. Yinjie Wang @YinjieW2024 , Xuyang Chen, Xiaolong Jin @JxlDragon , Mengdi Wang @MengdiWang10 , Ling Yang @LingYang_PU OpenClaw; SGLang; Megatron arxiv.org/pdf/2603.10165

Hybrid Self-evolving Structured Memory for GUI Agents The paper introduces Hybrid Self-evolving Structured Memory (HYMEM), a novel graph-based memory architecture that enhances GUI agents’ performance in long-horizon tasks by combining discrete symbolic nodes with continuous trajectory embeddings, enabling efficient multi-hop retrieval and adaptive updates. Sibo Zhu, Wenyi Wu, Kun Zhou, Stephen Wang, Biwei Huang @huang_biwei University of California, San Diego; Abel.ai arxiv.org/pdf/2603.10291

HATS: Hardness-Aware Trajectory Synthesis for GUI Agents HATS is a closed-loop framework that enhances the training of GUI agents by improving trajectory data quality through targeted exploration of semantically ambiguous actions and alignment refinement, significantly outperforming existing methods. Rui Shao, Ruize Gao, Bin Xie, Yixing Li, Kaiwen Zhou, Shuai Wang, Weili Guan, Gongwei Chen Harbin Institute of Technology, Shenzhen; National University of Singapore; CNRS@CREATE; Shenzhen Loop Area Institute; Huawei Noah’s Ark Lab arxiv.org/pdf/2603.12138

MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning MM-CondChain is a benchmark designed to evaluate the deep compositional reasoning capabilities of multimodal large language models, revealing that even leading models struggle with complex visual workflows that require multi-layer verification of conditions. Haozhan Shen, Shilin Yan, Hongwei Xue, Shuaiqi Lu, Xiaojun Tang, Guannan Zhang, Tiancheng Zhao, Jianwei Yin Accio Team, Alibaba Group; Zhejiang University; ZJU-BJ arxiv.org/pdf/2603.12266

CUAAudit: Meta-Evaluation of Vision-Language Models as Auditors of Autonomous Computer-Use Agents This study evaluates Vision-Language Models as auditors for autonomous Computer-Use Agents, revealing their strong performance in simple environments but significant limitations in complex settings, highlighting the need for more robust evaluation methods to ensure safe deployment. Marta Sumyk, Oleksandr Kosovan Ukrainian Catholic University arxiv.org/pdf/2603.10577