MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments

[2602.06075] MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments Skip to main content Search Submit Donate Log in Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2602.06075 (cs) [Submitted on 3 Feb 2026 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: MemGUI-Bench: Benchmarking Memory of Mobile GUI Agents in Dynamic Environments Authors: Guangyi Liu , Pengxiang Zhao , Yaozhen Liang , Qinyi Luo , Shunye Tang , Yuxiang Chai , Weifeng Lin , Han Xiao , WenHao Wang , Siheng Chen , Zhengxi Lu , Gao Wu , Hao Wang , Liang Liu , Yong Liu View PDF HTML (experimental) Abstract: Reliable mobile GUI agents must retain and reuse information across actions, applications, and repeated interactions. However, current benchmarks systematically underrepresent these memory demands: only 5.2-11.8 percent of their tasks are memory-related, and none evaluates cross-session learning. We introduce MemGUI-Bench, a comprehensive memory-centric benchmark that assesses both short-term information retention and long-term experience accumulation through pass@k protocols and staged LLM-as-judge evaluation. Our contributions include: (1) a systematic taxonomy of short- and long-term memory based on 11 agents across 5 architectures; (2) a snapshot-based suite of 128 tasks across 26 applications, organized into 64 mirror pairs, where 89.8 percent require cross-temporal and cross-spatial retention; (3) MemGUI-Eval, an automated 3-stage Progressive Scrutiny pipeline with 7 hierarchical metrics spanning memory fidelity, learning effectiveness, and execution efficiency; and (4) an assessment of 11 state-of-the-art agents guided by 6 research questions. Our experiments reveal substantial memory deficits across all evaluated systems, including 4-10x capability gaps on memory-intensive tasks. They further show that short-term memory is indispensable, while explicit long-term memory improves cross-session learning by 21.9 percentage points, with cross-application transfer and computational cost remaining major bottlenecks. We additionally identify 5 distinct failure modes and synthesize 5 actionable design implications for future memory-enhanced agents. All resources, including code, benchmark, and evaluation results, will be fully open-sourced and continuously maintained at this https URL . Comments: Accepted to ACM MM 2026. Project page: this https URL Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2602.06075 [cs.DC] (or arXiv:2602.06075v3 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2602.06075 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Liu Guangyi [ view email ] [v1] Tue, 3 Feb 2026 17:01:59 UTC (17,269 KB) [v2] Tue, 11 Aug 2026 13:42:35 UTC (40,127 KB) [v3] Thu, 27 Aug 2026 06:48:18 UTC (39,945 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.DC < prev | next > new | recent | 2026-02 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• ParaCell: Paravirtualized Secure Containers with Lightweight Intra-Container Isolation and Intent-Driven Memory Management#216920
• Android Uygulaması Testi için İdeal Test Ön Çalışması#1027146
• Vestrum: Improving Agent Harnesses by Adapting Their Verification, Structure and Memory#1118895
• LongPIBench: A Long-Context Benchmark for Prompt Injection#618502
• Age-related differences in the evaluation of a virtual health agent’s appearance and embodiment in a health-related interaction: Experimental lab study#657436