DeltaReplay is a step‑level memory reuse framework for mobile GUI agents that decides how to use stored execution trajectories without modifying them. It stores trajectories as paths in a transition graph, separating each action into a task‑independent operation and task‑specific parameters, and compares each recorded step with the new task and current screen to decide whether to follow, adapt, or ignore it. Experiments on AndroidWorld and SPA‑Bench show that DeltaReplay can increase task success rates by up to 10.3 and 25.0 percentage points over a base agent with the same backbone.
By Yudong Bai, Yihong Chen, Quanming Yao, Yaqing Wang
arXiv:2511.03728v2 Announce Type: replace
Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memo...
By Sanidhya Vijayvargiya, Rahul Lokesh
DeltaReplay introduces a step-level memory reuse framework for mobile GUI agents that selectively reuses parts of stored execution trajectories without modifying them. By representing trajectories as paths in a transition graph and separating actions into task-independent operations and task-specific parameters, the system compares each recorded step with the new task and current screen to decide whether to follow, adapt, or ignore it. Experiments on AndroidWorld and SPA-Bench show that this selective reuse can boost task success rates by up to 10.3 and 25.0 percentage points over a baseline agent.
arXiv:2607. 13027v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action.
By Hongru Cai, Yongqi Li, Ran Wei, Wenjie Li
JarvisGUI is a new benchmark that tests GUI agents on cross-device workflows involving Android, Windows, and Ubuntu, requiring transfer of intermediate results and coordination across heterogeneous platforms. It formulates tasks as input-output transformations under a lightweight type system, enabling automatic composition of multi-step, cross-device workflows and dynamic evaluation within a unified framework. The benchmark reveals that state-of-the-art open-source GUI agents struggle with state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management, exposing a critical capability gap invisible to existing benchmarks.
By Zixiang Chen, Yuheng Lu, Zihao Cheng, Zeming Liu, Jizeng Bai, Ziye Huang, Zhiyin Lin, Zihan Li, Yuhang Guo, Yunhong Wang, Haifeng Wang
EchoPath is a model‑agnostic framework that transforms validated GUI interaction trajectories into standardized, parameter‑controlled memories, enabling agents to replay specific GUI actions deterministically. Each memory records task intent, preconditions, input parameters, GUI evidence, validation provenance, and lifecycle state, and uses an image‑based target‑reaiming algorithm to adjust coordinates for the current screen before execution. Experiments on real computer‑use tasks show that EchoPath cuts median token cost by over 90 % and median execution time by about 60 %.
By Yao Zhao, Aditya Shanmugham, Swastik Roy, Yanxun Xu
arXiv:2606. 17929v1 Announce Type: new Abstract: Computer-using agents drive real software through the screen -- clicking and typing -- but they solve every task from scratch: asked to repeat a task, an agent re-reads the screen, re-reasons every tap, and pays the full cost again.
By Bojie Li
GUI-CC is a benchmark designed to assess the contextual consistency of GUI world models when used as agent environments, rather than just one‑step next‑screen predictors. It includes two tracks: an offline reference‑action track that rolls models along real mobile GUI trajectories, and an online agent‑loop track where fixed probing agents interact with model‑generated UIs. The benchmark evaluates transition fidelity, plausibility, contextual consistency, and task progress across 500 offline trajectory tasks and 200 online tasks spanning 30 mobile apps.
By Lin Fu, Zheyuan Yang, Tianhui Zhang, Jinbiao Wei, Guo Gan, Boxu Liu, Yilun Zhao, Yu Rong
MobileGym is a browser-hosted, lightweight simulation platform designed for mobile GUI agent research. It offers verifiable outcome signals via deterministic, JSON-based state judging and supports scalable online reinforcement learning with hundreds of parallel instances on a single server. The platform includes a declarative task-definition framework, a structured AnswerSheet protocol, and a benchmark of 416 parameterized tasks across 28 apps, demonstrating strong sim-to-real transfer in a case study.
By Dingbang Wu, Rui Hao, Haiyang Wang, Shuzhe Wu, Han Xiao, Zhenghong Li, Bojiang Zhou, Zheng Ju, Zichen Liu, Lue Fan, Zhaoxiang Zhang
arXiv:2609.37743v1 Announce Type: new
Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even...
By Savini Kashmira, Jayanaka L. Dantanarayana, Lingjia Tang, Jason Mars
APPSim-Bench is a new benchmark for mobile GUI agents that uses controllable simulated apps to balance realism and reproducibility. It includes 557 tasks across 17 popular Chinese and English apps, with a coding-agent-assisted and human-verified workflow that ensures deterministic evaluation. Evaluation of 19 agents shows that autonomous mobile execution is still far from perfect, with the best model completing only 50.27% of tasks and many failures in longer workflows and numerical reasoning.
By Jintian Feng, Long Chen, Xiao Yu, Jiayi Dai, Chenglong Liu, Haoru Wang, Zizhen Xue, Yuxuan Shi, Ziyang Wang, Yichen Gong
arXiv:2607. 13465v1 Announce Type: cross Abstract: LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes.
By Huatao Li, Xinwei Geng, Yuheng Wang, Yutong Li, Runde Yang, Hantao Chen, Shu Yao, Jingru Fan, Xuhui Ren, Yuanyuan Zhao, Fei Huang, Chen Qian