Reflection with Action-Induced Visual Differences for Desktop GUI Agents

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:2605.19260v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have recently emerged as promising backbones for GUI-agent models, where high-resolution GUI

arXiv:2604.13531v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks

arXiv:2605.01208v1 Announce Type: new Abstract: Vision-language model based graphical user interface (GUI) agents have shown strong interaction capabilities. However, they often

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

arXiv:2604.04990v1 Announce Type: cross Abstract: AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural

arXiv:2606.31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain