iPrefBench tests when mobile agents should infer or ask
Seven frontier agents hit up to 91.6% action completion but only 47.7% full task success.
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Seven frontier agents hit up to 91.6% action completion but only 47.7% full task success.
MobileInject benchmarks MLLM mobile agents against multimodal injection across 108 risk instances, finding broad vulnerability.
The 520-task suite spans 14 risk categories and reports 100% ASR on Qwen3.5-122B after dynamic evolution.
Authors report AndroidWorld 79.9, MobileWorld 50.4, and SPA-Bench rising from 31.9 to 59.6.
Jev-Mobile splits VLM planning from fast GUI execution, cutting AndroidWorld API cost 73% at similar success.
Qwen-Planner-Agent uses a closed-loop AI-for-AI pipeline and leads MobilePA-Bench for mobile planning.
Researchers expose 'human-agent UI desynchronization' attacks where repackaged APKs invisibly mislead mobile AI agents into attacker-chosen actions.
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