34 citations · 43 across the 13 of their papers we have counts for
4 papers · 1 filter
InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk
Yuan Gao, Zeren Yang, Junnan Li +6
Managing modern computing infrastructure has become a steadily harder problem due to the ever-increasing complexity. Recent advances in AI agents create a timely opportunity to aut…
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
Chenhao Ye, Huaizheng Zhang, Mingcong Han +11
Modern LLM reinforcement learning (RL) workloads require a high-performance weight transfer system to scale training across heterogeneous compute resources. However, efficiently tr…
Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems
Shawn Wanxiang Zhong, Junxuan Liao, Jing Liu +3
AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of…
OBASE: Object-Based Address-Space Engineering to Improve Memory Tiering
Vinay Banakar, Suli Yang, Kan Wu +3
Hardware and OS mechanisms for memory tiering are widely deployed, yet datacenters still overprovision DRAM. The root cause is hotness fragmentation: allocators place objects by si…