✨GUI-360: A Comprehensive Dataset and Benchmark for Computer-Using Agents

📝 Summary:
GUI-360 is a large dataset and benchmark for computer-using agents, addressing gaps in real-world tasks and unified evaluation. It contains over 1.2M action steps in Windows apps for GUI grounding, screen parsing, and action prediction. Benchmarking reveals significant shortcomings in current mod...

🔹 Publication Date: Published on Nov 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.04307
• PDF: https://arxiv.org/pdf/2511.04307

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#AI #ComputerAgents #GUIAgents #Dataset #Benchmark

✨OmniParser for Pure Vision Based GUI Agent

📝 Summary:
OmniParser enhances GPT-4V's ability to act as a GUI agent by improving screen parsing. It identifies interactable icons and understands element semantics using specialized models. This significantly boosts GPT-4V's performance on benchmarks like ScreenSpot, Mind2Web, and AITW.

🔹 Publication Date: Published on Aug 1, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2408.00203
• PDF: https://arxiv.org/pdf/2408.00203
• Github: https://github.com/microsoft/omniparser

🔹 Models citing this paper:
• https://huggingface.co/microsoft/OmniParser
• https://huggingface.co/microsoft/OmniParser-v2.0
• https://huggingface.co/banao-tech/OmniParser

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mlfoundations/Click-100k

✨ Spaces citing this paper:
• https://huggingface.co/spaces/callmeumer/OmniParser-v2
• https://huggingface.co/spaces/nofl/OmniParser-v2
• https://huggingface.co/spaces/SheldonLe/OmniParser-v2

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#GUIagents #ComputerVision #GPT4V #AIagents #DeepLearning

✨HiconAgent: History Context-aware Policy Optimization for GUI Agents

📝 Summary:
HiconAgent introduces History Context-aware Policy Optimization HCPO for GUI agents. HCPO efficiently leverages historical context using dynamic sampling and compression, achieving better performance than larger models with reduced computational cost and significant speedups.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01763
• PDF: https://arxiv.org/pdf/2512.01763
• Github: https://github.com/JiuTian-VL/HiconAgent

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#HiconAgent #GUIAgents #AIResearch #ReinforcementLearning #ContextAwareAI

✨GUI Exploration Lab: Enhancing Screen Navigation in Agents via Multi-Turn Reinforcement Learning

📝 Summary:
GUI Exploration Lab is a simulation environment to train GUI agents for screen navigation. It finds supervised fine-tuning establishes basics, single-turn reinforcement learning improves generalization, and multi-turn RL enhances exploration for superior navigation performance.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02423
• PDF: https://arxiv.org/pdf/2512.02423

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#ReinforcementLearning #GUIAgents #AINavigation #MachineLearning #AIResearch

✨MAI-UI Technical Report: Real-World Centric Foundation GUI Agents

📝 Summary:
MAI-UI introduces a family of foundation GUI agents tackling real-world deployment challenges. It uses a self-evolving data pipeline, device-cloud collaboration, and online RL to set new state-of-the-art in GUI grounding and mobile navigation, significantly boosting performance and privacy.

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22047
• PDF: https://arxiv.org/pdf/2512.22047

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#GUIAgents #AI #ReinforcementLearning #MobileTech #HCI