GUIGuard-Bench is a new benchmark with annotated GUI screenshots that measures privacy recognition, planning fidelity under protection, and utility impact for trajectory-based GUI agents.
Caution for the environment: Multimodal agents are susceptible to environmental distractions
5 Pith papers cite this work. Polarity classification is still indexing.
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HLL is a new benchmark that evaluates eight frontier multimodal agents on closed-loop interactive CAPTCHA solving, showing sharp performance drops under realism stressors and trace validation.
The work creates a new benchmark for humanizing GUI agent touch dynamics via a MinMax detector-agent model, a mobile touch dataset, and methods showing agents can match human behavior without losing task performance.
Introduces an app-content instrumentation framework and benchmark showing that examined GUI agents suffer 42.0% and 36.1% average misleading rates from third-party content in dynamic and static tests respectively.
LaSM is a layer-wise scaling mechanism that amplifies attention and MLP modules in critical layers to defend GUI agents against pop-up attacks by correcting attention misalignment.
citing papers explorer
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GUIGuard-Bench: Toward a General Evaluation for Privacy-Preserving GUI Agents
GUIGuard-Bench is a new benchmark with annotated GUI screenshots that measures privacy recognition, planning fidelity under protection, and utility impact for trajectory-based GUI agents.
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HLL: Can Agents Cross Humanity's Last Line of Verification?
HLL is a new benchmark that evaluates eight frontier multimodal agents on closed-loop interactive CAPTCHA solving, showing sharp performance drops under realism stressors and trace validation.
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Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
The work creates a new benchmark for humanizing GUI agent touch dynamics via a MinMax detector-agent model, a mobile touch dataset, and methods showing agents can match human behavior without losing task performance.
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Mobile GUI Agents under Real-world Threats: Are We There Yet?
Introduces an app-content instrumentation framework and benchmark showing that examined GUI agents suffer 42.0% and 36.1% average misleading rates from third-party content in dynamic and static tests respectively.
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LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents
LaSM is a layer-wise scaling mechanism that amplifies attention and MLP modules in critical layers to defend GUI agents against pop-up attacks by correcting attention misalignment.