activity
20232026
most citedRackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design

3 citations · 5 across the 9 of their papers we have counts for

collaborators

10 papers

cs.DC2026

Fast Recovery for LLM Serving via Decoupled Device Memory Lifetime in Dynamo

Schwinn Saereesitthipitak, Mohammed Abdulwahhab, Hannah Zhang +6

Large language model (LLM) inference replicas run across tightly coupled GPUs and serve traffic continuously for weeks. Hardware and software failures are therefore inevitable, and…

cs.IR2026

IRB: Automated Generation of Robust Factuality Benchmarks

Lam Thanh Do, Bhagyashree Taleka, Hozaifa Ammar Bhutta +3

Static benchmarks for RAG systems often suffer from rapid saturation and require significant manual effort to maintain robustness. To address this, we present IRB, a framework for…

cs.SE2026

VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents

Bing Xu, Terry Chen, Fengzhe Zhou +12

VIBETENSOR is an open-source research system software stack for deep learning, generated by LLM-powered coding agents under high-level human guidance. In this paper, "fully generat…

cs.AR2025

Five-Minute Rule 40 Years Later: A First-Principles Revisit for Modern Memory Hierarchy

Tong Zhang, Vikram Sharma Mailthody, Fei Sun +7

In 1987, Jim Gray and Gianfranco Putzolu introduced the five-minute rule, a simple, storage-memory-economics-based heuristic for deciding when data should live in DRAM rather than…

cs.AI2025

AUGUSTUS: An LLM-Driven Multimodal Agent System with Contextualized User Memory

Jitesh Jain, Shubham Maheshwari, Ning Yu +2

Riding on the success of LLMs with retrieval-augmented generation (RAG), there has been a growing interest in augmenting agent systems with external memory databases. However, the…

cs.CV2025

Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light

Ali Hassani, Fengzhe Zhou, Aditya Kane +13

Many sparse attention mechanisms such as Neighborhood Attention have typically failed to consistently deliver speedup over the self attention baseline. This is largely due to the l…