activity
20202026
most citedIs Network the Bottleneck of Distributed Training?

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

collaborators
Showing 2026Show all

5 papers · 1 filter

cs.OS2026

Capture the lifecycle: KV Cache management in ReAct Agents with KVTether

Kaihua Fu, Yukun Zhou, Chaokun Chang +6

Efficient serving of long-context reasoning-and-acting (ReAct) agents relies on KV cache reuse to reduce large language model (LLM) prefill latency and monetary cost. However, a se…

cs.AI2026

AgentPProf: Semantic Profiler for Long Horizon AI Agents

Yusheng Zheng, Chaokun Chang, Yu Mao +7

AI agents increasingly orchestrate long-running activities with users, tools, and system resources for days and weeks. To improve agent quality, safety, and cost efficiency, develo…

cs.OS2026

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

Chaokun Chang, Yukun Zhou, Kaihua Fu +10

Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state. However, th…

cs.CV2026

ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling

Shaobo Ju, Baiyang Song, Tao Chen +6

Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable progress in image-language tasks. How…

cs.CV2026

Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism

Tao Chen, Kun Zhang, Qiong Wu +5

Long video understanding is a key challenge that plagues the advancement of \emph{Multimodal Large language Models} (MLLMs). In this paper, we study this problem from the perspecti…