EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents
arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains
arXiv:2605.29119v1 Announce Type: new Abstract: Computer use agents (CUAs) have shown strong potential for automating complex digital workflows, yet their training remains
arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile
arXiv:2605.19447v1 Announce Type: new Abstract: Reinforcement learning can train LLM agents from sparse task rewards, but long-horizon credit assignment remains challenging: a
arXiv:2604.09815v1 Announce Type: new Abstract: Computer-use agents that combine GUI interaction with structured API calls via the Model Context Protocol (MCP) show promise for
arXiv:2604.27151v1 Announce Type: new Abstract: Computer-use agents provide a promising path toward general software automation because they can interact directly with arbitrary
arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment
arXiv:2603.27195v1 Announce Type: new Abstract: Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This
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