arXiv AI

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud

arXiv:2608. 03682v1 Announce Type: new Abstract: Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment.

arXiv AI

Jun 9

vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models

arXiv:2606. 08094v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation-class GPU, a mismatch for the hardware on which robots actually run.

By Khanh D. Nguyen, Hung T. Ho, Chinh T. Nguyen, Thanh Q. Duong, Linh D. Le, Duy M. H. Nguyen, Vien A. Ngo, An T. Le

llms diffusion robotics multimodal benchmarks

arXiv AI

Jul 15

Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference

arXiv:2607. 12659v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks.

By Zebin Yang, Qi Wang, Yunhe Wang, Xiurui Guo, Bo Yu, Shaoshan Liu, Jiafeng Xu, Hao Dong, Meng Li

llms robotics efficiency multimodal benchmarks

arXiv Machine Learning

Jun 19

Execution-State Capsules: Graph-Bound Execution-State Checkpoint and Restore for Low-Latency, Small-Batch, On-Device Physical-AI Serving

arXiv:2606. 20537v1 Announce Type: new Abstract: Mainstream LLM serving systems reuse prefix work mainly through paged or radix key-value (KV) caches.

By Liang Su

llms agents robotics efficiency benchmarks data-engineering

arXiv AI

Jun 16

RollArt: Disaggregated Multi-Task Agentic RL Training at Scale

arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.

By Wei Gao, Yuheng Zhao, Tianyuan Wu, Shaopan Xiong, Weixun Wang, Dakai An, Lunxi Cao, Dilxat Muhtar, Zichen Liu, Haizhou Zhao, Ju Huang, Siran Yang, Yongbin Li, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng, Wei Wang

llms agents reinforcement-learning efficiency

arXiv AI

6d ago

Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control

arXiv:2608. 12123v1 Announce Type: cross Abstract: LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect.

By Josef Liyanjun Chen

llms agents

arXiv AI

1d ago

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

llms agents benchmarks

arXiv AI

Jun 9

AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving

arXiv:2606. 09613v1 Announce Type: cross Abstract: Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution.

By Rakibul Hasan Rajib, Mengxin Zheng, Qian Lou

llms agents

arXiv AI

Jul 28

VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference

arXiv:2512. 01031v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks.

By Jiaming Tang, Yufei Sun, Yilong Zhao, Shang Yang, Yujun Lin, Zhuoyang Zhang, James Hou, Yao Lu, Zhijian Liu, Song Han

robotics efficiency multimodal benchmarks

arXiv AI

Jun 10

Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design

arXiv:2606. 10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-concurrency workloads.

By Wenxin Wang, Yule Hou, Yu Ji, Peng Qu, Youhui Zhang

efficiency

arXiv AI

Jun 16

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.

By Yicheng Feng, Xin Tan, Yangtao Deng, Yimin Jiang, Yibo Zhu, Hong Xu

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Jul 16

TuxBot: Semantic-Aware Online OS Tuning with Large Language Models

arXiv:2605. 15026v2 Announce Type: replace-cross Abstract: Online OS tuning can improve long-running services, but existing controllers are poorly matched to live hosts.

By Georgios Liargkovas, Mihir Nitin Joshi, Hubertus Franke, Kostis Kaffes

llms benchmarks

arXiv AI

1d ago

StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

arXiv:2608. 15089v1 Announce Type: new Abstract: Long-horizon agents can fail even when their underlying models can solve the constituent steps.

By Ziheng Qin, Yaxin Lu, Zhangyang Atlas Wang, Kai Wang

llms agents

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