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New framework boosts MLLM mobile agents' confidence and task success

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Mobile-Aptus, a confidence-driven framework designed to improve the interaction capabilities of multimodal large language model (MLLM)-based mobile agents. This framework addresses issues of over-execution and over-soliciting by empowering agents to output confidence scores alongside actions and then correcting these scores using semantic similarity and direct preference optimization. Mobile-Aptus has demonstrated state-of-the-art performance across four benchmarks, showing significant improvements in task success rates and reducing the need for human intervention. AI

IMPACT Enhances the reliability and efficiency of AI agents operating on mobile devices, reducing unnecessary human intervention.

RANK_REASON The cluster contains a research paper detailing a new framework for MLLM-based agents.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework boosts MLLM mobile agents' confidence and task success

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The cluster contains a research paper detailing a new framework for MLLM-based agents.
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134 days old
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Zheng Wu, Pengzhou Cheng, Zongru Wu, Yuan Guo, Tianjie Ju, Aston Zhang, Gongshen Liu, Zhuosheng Zhang ·

    Mobile-Aptus: Confidence-Driven Proactive and Robust Interaction in MLLM-based Mobile-Using Agents

    arXiv:2605.28629v1 Announce Type: new Abstract: Recent advancements in multimodal large language models (MLLMs) have shown exceptional potential in enabling mobile-using agents to autonomously execute human instructions. However, fully automated agents often try to execute tasks …

  2. arXiv cs.CL TIER_1 English(EN) · Zhuosheng Zhang ·

    Mobile-Aptus: Confidence-Driven Proactive and Robust Interaction in MLLM-based Mobile-Using Agents

    Recent advancements in multimodal large language models (MLLMs) have shown exceptional potential in enabling mobile-using agents to autonomously execute human instructions. However, fully automated agents often try to execute tasks even when they are unable to resolve them, leadi…

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