arXiv AI By Zixu Hao, Weijun Wang, Xiangyu Li, Liang Mi, Hao Wu, Yunxin Liu, Ting Cao

Replicate What Varies: Resource Sharing for Domain-Randomized Embodied Simulation

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Patchwork is a resource‑sharing system for domain‑randomized embodied simulation that separates a shared template of common resources from patches that record environment‑specific mutations. By sharing compiled model structure and immutable assets while keeping mutable parameters private, Patchwork reduces CPU memory usage dramatically—up to 92% at 32 replicas—compared to running each environment in its own process. The approach was implemented on robosuite and MuJoCo and evaluated on LIBERO‑plus and RoboCasa365 workloads, demonstrating that memory growth shifts toward template diversity rather than instance count.

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arXiv AI
Aug 18

From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems

arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.

By Chaokun Chang, Yukun Zhou, Kaihua Fu, Dakai An, Tianyu Feng, Hanfeng Lu, Sheng Yao, Pu Guo, Yinghao Yu, Yizhou Shan, Bo Li, Binhang Yuan, Wei Wang
arXiv AI
Sep 24

MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research

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