@inproceedings{jang-etal-2026-mate,
title = "{MATE}: Mobile Agent Trustworthy Execution for Safe and Efficient Mobile Control",
author = "Jang, Minsuk and
Kim, Hee-Seon and
Yu, Hui-Cheol and
Kim, Changick",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1820/",
pages = "38874--38895",
ISBN = "979-8-89176-462-0",
abstract = "Mobile agents on personal smartphones can affect private data, payments, settings, and the foreground screen. We present \textbf{MATE} (Mobile Agent Trustworthy Execution), an execution-layer system that controls the boundary between agent action proposals and device effects without modifying the underlying model. MATE represents each UI primitive as a structured action record that combines the instruction, target app, screen context, primitive, and action fields. Runtime Guardrail (RG) maps this record to an allow, block, or user-approval decision; Predefined Task Routing (PTR) serves recurring requests through verified routes with post-execution screen checks; and a floating interface keeps execution in a compact overlay. On MobileSafetyBench, MATE raises high-risk blocking from 29.7{\%} to 97.0{\%} under GPT-4o. On AndroidWorld, it reduces LLM calls by up to 6 and raises recurring-use task success to 95.7{\%}."
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<abstract>Mobile agents on personal smartphones can affect private data, payments, settings, and the foreground screen. We present MATE (Mobile Agent Trustworthy Execution), an execution-layer system that controls the boundary between agent action proposals and device effects without modifying the underlying model. MATE represents each UI primitive as a structured action record that combines the instruction, target app, screen context, primitive, and action fields. Runtime Guardrail (RG) maps this record to an allow, block, or user-approval decision; Predefined Task Routing (PTR) serves recurring requests through verified routes with post-execution screen checks; and a floating interface keeps execution in a compact overlay. On MobileSafetyBench, MATE raises high-risk blocking from 29.7% to 97.0% under GPT-4o. On AndroidWorld, it reduces LLM calls by up to 6 and raises recurring-use task success to 95.7%.</abstract>
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%0 Conference Proceedings
%T MATE: Mobile Agent Trustworthy Execution for Safe and Efficient Mobile Control
%A Jang, Minsuk
%A Kim, Hee-Seon
%A Yu, Hui-Cheol
%A Kim, Changick
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jang-etal-2026-mate
%X Mobile agents on personal smartphones can affect private data, payments, settings, and the foreground screen. We present MATE (Mobile Agent Trustworthy Execution), an execution-layer system that controls the boundary between agent action proposals and device effects without modifying the underlying model. MATE represents each UI primitive as a structured action record that combines the instruction, target app, screen context, primitive, and action fields. Runtime Guardrail (RG) maps this record to an allow, block, or user-approval decision; Predefined Task Routing (PTR) serves recurring requests through verified routes with post-execution screen checks; and a floating interface keeps execution in a compact overlay. On MobileSafetyBench, MATE raises high-risk blocking from 29.7% to 97.0% under GPT-4o. On AndroidWorld, it reduces LLM calls by up to 6 and raises recurring-use task success to 95.7%.
%U https://aclanthology.org/2026.findings-emnlp.1820/
%P 38874-38895
Markdown (Informal)
[MATE: Mobile Agent Trustworthy Execution for Safe and Efficient Mobile Control](https://aclanthology.org/2026.findings-emnlp.1820/) (Jang et al., Findings 2026)
ACL