arXiv:2607. 15193v1 Announce Type: new Abstract: Graphical user interface (GUI) automation remains challenging in real-world environments, where dynamic layouts, unexpected dialogs, and evolving interface states can cause autonomous agents to drift from user intent.
GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step workflows. However, the diversity of available tr...
arXiv:2610.01215v1 Announce Type: new Abstract: GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step...
arXiv:2607. 01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently.
arXiv:2606. 11042v1 Announce Type: new Abstract: Recent years have witnessed the rapid evolution of AI agents toward handling increasingly complex, real-world tasks.
arXiv:2606. 07594v1 Announce Type: new Abstract: Personal AI agents must increasingly operate across APIs, shells, web surfaces, and desktop GUIs, yet many systems remain tuned to a single interface and offer limited support for user teaching and auditability.
The paper introduces Evidence-First Reflection (EFR), a two-stage reflector for desktop GUI agents that separates visual difference extraction from outcome verification. EFR uses Set-of-Marks annotations to locate action sites and candidate changed regions, filters action-relevant changes, and then makes a final judgment based on cleaned evidence. Experiments on OSWorld-Verified and WindowsAgentArena show that EFR improves reflector accuracy by 7.11% and increases end-to-end task success by roughly 5–6%.
The paper introduces CONFLICTGUI, a benchmark that tests GUI agents on instruction-internal and instruction‑GUI context conflicts, revealing that many agents over‑comply with infeasible instructions. To address this, the authors propose CONFLICTGUARD, an inference‑time framework that couples a feasibility verification protocol with a conditional action modulation mechanism, enabling agents to assess instruction logic and GUI evidence before acting. Experiments on five agents show that CONFLICTGUARD significantly improves conflict‑task success while maintaining normal task performance.
VLAA-GUI is a modular framework for autonomous GUI agents that addresses early stopping and repetitive loops by integrating three core components: a Completeness Verifier, a Loop Breaker, and an on-demand Search Agent. The framework also includes optional Coding and Grounding Agents for specialized tasks. Evaluations on five backbones across Linux and Windows benchmarks show strong performance, with some models surpassing human results and the Loop Breaker significantly reducing wasted steps.
arXiv:2607. 11818v1 Announce Type: cross Abstract: We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents.
arXiv:2609.24094v1 Announce Type: cross Abstract: Visual analytics (VA) enables sensemaking through interactive visualization, but effective analysis often requires experts to translate high-level in...
arXiv:2604. 27996v3 Announce Type: replace Abstract: This paper examines how large language model (LLM) agents perform on scientific visualization (SciVis) tasks that require generating visualization workflows from natural-language instructions.
arXiv:2506. 17913v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents have made significant progress in automating digital tasks through the utilization of computer vision and language models.