11 papers
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
Jiahao Wang, Kaizhan Lin, Kaixi Zhang +7
LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving--with LLM requests issued by agents instead of humans. This shifts the w…
CoAgent: Concurrency Control for Multi-Agent Systems
Hongtao Lyu, Dingyan Zhang, Mingyu Wu +2
Multi-agent LLM systems -- coding agents, devops agents, document agents -- now routinely run several agents in parallel against the same git tree, Kubernetes cluster, or document.…
Simple is Better: Multiplication May Be All You Need for LLM Request Scheduling
Dingyan Zhang, Jinbo Han, Kaixi Zhang +6
High-quality LLM request scheduling requires meeting two key objectives: ensuring the routed instance has KVCache to accelerate request execution, and ensuring that the workload is…
Efficient Vector Search in the Wild: One Model for Multi-K Queries
Yifan Peng, Jiafei Fan, Xingda Wei +7
Learned top-K search is a promising approach for serving vector queries with both high accuracy and performance. However, current models trained for a specific K value fail to gene…
KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider
Jiahao Wang, Jinbo Han, Xingda Wei +6
Serving large language models (LLMs) is important for cloud providers, and caching intermediate results (KV$) after processing each request substantially improves serving throughp…
Fast LLM Post-training via Decoupled and Fastest-of-N Speculation
Rongxin Cheng, Kai Zhou, Xingda Wei +8
Rollout dominates the training time in large language model (LLM) post-training, where the trained model is used to generate tokens given a batch of prompts. This work, SpecActor,…