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.
Omniact: A dataset and benchmark for enabling multimodal generalist autonomous agents for desktop and web
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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ScaleWoB generates 100+ synthetic interactive GUI environments and 1000+ verifiable tasks as web pages, releasing a 120-task mobile benchmark where state-of-the-art agents achieve 27.92% success (17.82% on long-horizon tasks) versus 92.08% for humans, with synthetic results generalizing to real apps
AndroidWorld is a dynamic, reproducible Android benchmark that generates unlimited natural-language tasks for autonomous agents and shows current agents succeed on only 30.6 percent of them.
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.
Aguvis presents a pure vision-based framework for autonomous GUI agents using structured reasoning via inner monologue, a new multimodal dataset, and two-stage training to reach SOTA on offline and online benchmarks.
OS-Atlas, trained on the largest open-source cross-platform GUI grounding corpus of 13 million elements, outperforms prior open-source models on six benchmarks across mobile, desktop, and web platforms.
WebCanvas creates a dynamic benchmark for web agents with a noise-resistant evaluation metric, the Mind2Web-Live dataset of 542 tasks, and open-source tools and agent framework for ongoing online testing.
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.
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.
citing papers explorer
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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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Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction
Aguvis presents a pure vision-based framework for autonomous GUI agents using structured reasoning via inner monologue, a new multimodal dataset, and two-stage training to reach SOTA on offline and online benchmarks.