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.
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:2609.39915v1 Announce Type: new
Abstract: Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents...
By Haoxiang Shi, Zaijing Li, Muhe Ding, Xiang Deng, Yaowei Wang, Liqiang Nie
arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.
By Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried
arXiv:2606. 09399v1 Announce Type: new Abstract: We present SUPERBROWSER, an autonomous web-navigation agent designed against a single guiding hypothesis: a web agent should browse the way a person browses.
By Radeen Mostafa, Sawradip Saha
arXiv:2511. 12997v2 Announce Type: replace Abstract: Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex browsing tasks across diverse domains.
By Genglin Liu, Shijie Geng, Sha Li, Hejie Cui, Sarah Zhang, Xin Liu, Tianyi Liu
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
By Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen, Lang Yin, Ze Chen, Tong Arthur Wu, Siru Ouyang, Zihan Wang, Jiaxin Pei, Julian McAuley, Yejin Choi, Alex Pentland
arXiv:2608.23035v1 Announce Type: new
Abstract: As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capabili...
By Yi Zhu, Xiongwei Wu, Qiyi Wang, Tingyu Qu, Jiajun Liu, Sihan Cao, Long Chen, Weigao Sun, Feida Zhu, Yiran Zhong, Steven Hoi
arXiv:2607. 10608v1 Announce Type: new Abstract: Memory is becoming a core component of long-horizon AI agents, allowing agents to reuse past experience when operating web browsers, software tools, and other interactive environments.
By Yixiong Chen, Xinyi Bai, Alan Yuille
The paper surveys efficient GUI agents, emphasizing that practical deployment requires more than task success—it must also minimize context, computation, action budget, and runtime overhead. It reviews key efficiency dimensions—observation, memory, action, and planner/system—and identifies recurring strategies such as selective reading, global-to-local visual allocation, recoverable memory, verification-aware control, and hybrid runtimes. The authors highlight open challenges, including accurate verifier cost accounting, benchmark comparability, and co-design of observation, memory, and execution layers under real latency and privacy constraints.
By Bizhe Bai, Jiakang Yuan, Hongming Wu, Xinyue Wang, Jie Ren, Siyao Chen, Yuchen Ya, Fan Bai, Pai Peng, Huafeng Qin, Tao Chen
The paper introduces a framework that selectively reuses parts of previously executed mobile GUI control traces to reduce LLM inference overhead. It identifies six categories of variation that can affect trace reuse and shows that individual transitions can still be valid when checked against the current task context and UI state. Using graph-text templates, the system achieves roughly 80% fewer LLM tokens and up to 2‑fold speedups on Android and HarmonyOS devices.
By Bingyu Bai, Cheng Zhang, Erhu Feng
arXiv:2510. 19838v2 Announce Type: replace Abstract: Autonomous web agents powered by large language models (LLMs) show strong potential for performing goal-oriented tasks such as information retrieval, report generation, and online transactions.
By Shiqi He, Yue Cui, Xinyu Ma, Yaliang Li, Bolin Ding, Mosharaf Chowdhury