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    <abstract>Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) promise human-like interaction with software applications, yet long-horizon tasks remain challenging due to memory limitations. Existing approaches either truncate history or rely on simple textual summaries, which risk losing critical information when past visual details become necessary for future decisions. In this paper, we propose PAL-UI (Planning with Active Look-back), a novel framework that enables GUI agents to adaptively retrieve past observations when required. PAL-UI combines a dual-level summary agent, capturing both observation-level cues and action-level outcomes, with a dedicated retrieval tool that allows the agent to recall specific historical screenshots during planning. We curate a step-level instruction dataset of 8.6K samples from mobile GUI navigation trajectories and train PAL-UI-3B and PAL-UI-7B models based on Qwen2.5-VL. Extensive experiments show that PAL-UI significantly outperforms baseline models and prior methods in mobile GUI tasks, even under data-efficient settings. Moreover, PAL-UI exhibits strong cross-domain generalization, achieving notable improvements in web navigation without additional training. Our work highlights the potential of active memory retrieval for long-horizon planning capabilities of vision-based GUI agents.</abstract>
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%0 Conference Proceedings
%T PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents
%A Liu, Zikang
%A Li, Junyi
%A Zhao, Xin
%A Gao, Dawei
%A Li, Yaliang
%A Wen, Ji-Rong
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F liu-etal-2026-pal
%X Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) promise human-like interaction with software applications, yet long-horizon tasks remain challenging due to memory limitations. Existing approaches either truncate history or rely on simple textual summaries, which risk losing critical information when past visual details become necessary for future decisions. In this paper, we propose PAL-UI (Planning with Active Look-back), a novel framework that enables GUI agents to adaptively retrieve past observations when required. PAL-UI combines a dual-level summary agent, capturing both observation-level cues and action-level outcomes, with a dedicated retrieval tool that allows the agent to recall specific historical screenshots during planning. We curate a step-level instruction dataset of 8.6K samples from mobile GUI navigation trajectories and train PAL-UI-3B and PAL-UI-7B models based on Qwen2.5-VL. Extensive experiments show that PAL-UI significantly outperforms baseline models and prior methods in mobile GUI tasks, even under data-efficient settings. Moreover, PAL-UI exhibits strong cross-domain generalization, achieving notable improvements in web navigation without additional training. Our work highlights the potential of active memory retrieval for long-horizon planning capabilities of vision-based GUI agents.
%U https://aclanthology.org/2026.findings-aacl.13/
%P 244-256

Markdown (Informal)

[PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents](https://aclanthology.org/2026.findings-aacl.13/) (Liu et al., Findings 2026)

• PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents (Liu et al., Findings 2026)

ACL

• Zikang Liu, Junyi Li, Xin Zhao, Dawei Gao, Yaliang Li, and Ji-Rong Wen. 2026. PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 244–256, Hengqin, China. Association for Computational Linguistics.