OSWorld provides the first unified real-computer benchmark for open-ended multimodal agent tasks, exposing large performance gaps between humans and state-of-the-art LLM/VLM agents.
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Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, with full release of data and code planned.
MAS-Bench introduces 139 tasks, 88 predefined shortcuts, and 9 metrics to evaluate hybrid GUI-shortcut mobile agents, reporting up to 68.3% success and 39% efficiency gains over GUI-only baselines.
AOHP is an Android-based OS harness that reports 21% higher agent task completion, 52% lower token cost, and improved security compliance through agent-first system primitives.
Grounding-certainty-guided Adaptive Advantage and Dynamic Clipping improve continual GUI grounding over RFT baselines on ScreenSpot-V1/V2/Pro.
PageGuide is a browser extension that grounds LLM responses in webpage DOM elements via visual overlays for Find, Guide, and Hide modes, reporting performance gains over unaided browsing in a 94-user study.
VLAA-GUI adds mandatory visual verifiers, multi-tier loop breakers, and on-demand search to GUI agents, reaching 77.5% on OSWorld and 61.0% on WindowsAgentArena with some models exceeding human performance.
An anonymization framework replaces sensitive UI content with deterministic placeholders to protect privacy in mobile GUI agents while preserving task performance.
Presents a failure taxonomy and intervention-mapping methodology for industry-scale AVLM development in content moderation, applied to a global video platform system.
MUIAnno is an expert-annotated dataset of mobile UI screens from iOS apps with structured JSON labels and baseline results for UI element detection.
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
Proposes four architectural patterns—Hybrid Affordance Integration, Adaptive Visual Anchoring, Visual Hierarchy Synthesis, and Semantic Scene Graph—to balance non-determinism and latency of foundation models with enterprise requirements for determinism and real-time performance.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
citing papers explorer
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OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
OSWorld provides the first unified real-computer benchmark for open-ended multimodal agent tasks, exposing large performance gaps between humans and state-of-the-art LLM/VLM agents.
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MolmoWeb: Open Visual Web Agent and Open Data for the Open Web
Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, with full release of data and code planned.
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MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents
MAS-Bench introduces 139 tasks, 88 predefined shortcuts, and 9 metrics to evaluate hybrid GUI-shortcut mobile agents, reporting up to 68.3% success and 39% efficiency gains over GUI-only baselines.
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AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interaction
AOHP is an Android-based OS harness that reports 21% higher agent task completion, 52% lower token cost, and improved security compliance through agent-first system primitives.
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GUI-AC: Enhancing Continual Learning in GUI Agents
Grounding-certainty-guided Adaptive Advantage and Dynamic Clipping improve continual GUI grounding over RFT baselines on ScreenSpot-V1/V2/Pro.
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PageGuide: Browser extension to assist users in navigating a webpage and locating information
PageGuide is a browser extension that grounds LLM responses in webpage DOM elements via visual overlays for Find, Guide, and Hide modes, reporting performance gains over unaided browsing in a 94-user study.
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VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation
VLAA-GUI adds mandatory visual verifiers, multi-tier loop breakers, and on-demand search to GUI agents, reaching 77.5% on OSWorld and 61.0% on WindowsAgentArena with some models exceeding human performance.
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Anonymization-Enhanced Privacy Protection for Mobile GUI Agents: Available but Invisible
An anonymization framework replaces sensitive UI content with deterministic placeholders to protect privacy in mobile GUI agents while preserving task performance.
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From Failure Taxonomy to Intervention: A Diagnostic Methodology for Industry-Scale AVLM in Video and Live-Streaming Platform Moderation
Presents a failure taxonomy and intervention-mapping methodology for industry-scale AVLM development in content moderation, applied to a global video platform system.
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MUIAnno: An Expert-Annotated Dataset and Evaluation Benchmark for Mobile UI Understanding
MUIAnno is an expert-annotated dataset of mobile UI screens from iOS apps with structured JSON labels and baseline results for UI element detection.
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Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
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A Pattern Language for Resilient Visual Agents
Proposes four architectural patterns—Hybrid Affordance Integration, Adaptive Visual Anchoring, Visual Hierarchy Synthesis, and Semantic Scene Graph—to balance non-determinism and latency of foundation models with enterprise requirements for determinism and real-time performance.
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Toward Native Multimodal Modeling: A Roadmap
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
- Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding