OS-SPEAR is a new evaluation toolkit that tests 22 OS agents and identifies trade-offs between efficiency and safety or robustness.
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5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
AutoFocus converts token perplexity into an anisotropic Gaussian uncertainty field to drive region proposals and shape-aware zooming for improved GUI grounding in VLMs.
AutoGUI-v2 is a new benchmark exposing that VLMs handle basic GUI grounding but struggle with complex interaction logic and state prediction.
The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.
A co-evolving proposer-critic RL framework improves GUI grounding accuracy by letting the model critique its own proposals rendered on screenshots.
citing papers explorer
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OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents
OS-SPEAR is a new evaluation toolkit that tests 22 OS agents and identifies trade-offs between efficiency and safety or robustness.
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AutoFocus: Uncertainty-Aware Active Visual Search for GUI Grounding
AutoFocus converts token perplexity into an anisotropic Gaussian uncertainty field to drive region proposals and shape-aware zooming for improved GUI grounding in VLMs.
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AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark
AutoGUI-v2 is a new benchmark exposing that VLMs handle basic GUI grounding but struggle with complex interaction logic and state prediction.
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GUI Agents with Reinforcement Learning: Toward Digital Inhabitants
The paper delivers the first comprehensive overview of RL for GUI agents, organizing methods into offline, online, and hybrid strategies while analyzing trends in rewards, efficiency, and deliberation to outline a future roadmap.
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Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding
A co-evolving proposer-critic RL framework improves GUI grounding accuracy by letting the model critique its own proposals rendered on screenshots.