76 citations · 139 across the 25 of their papers we have counts for
8 papers · 1 filter
R-WoM: Retrieval-augmented World Model For Computer-use Agents
Kai Mei, Jiang Guo, Shuaichen Chang +4
Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentia…
EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning
Wujiang Xu, Wentian Zhao, Zhenting Wang +6
Training LLM agents in multi-turn environments with sparse rewards, where completing a single task requires 30+ turns of interaction within an episode, presents a fundamental chall…
LiteCUA: Computer as MCP Server for Computer-Use Agent on AIOS
Kai Mei, Xi Zhu, Hang Gao +2
We present AIOS 1.0, a novel platform designed to advance computer-use agent (CUA) capabilities through environmental contextualization. While existing approaches primarily focus o…
Cerebrum (AIOS SDK): A Platform for Agent Development, Deployment, Distribution, and Discovery
Balaji Rama, Kai Mei, Yongfeng Zhang
Autonomous LLM-based agents have emerged as a powerful paradigm for complex task execution, yet the field lacks standardized tools for development, deployment, distribution and dis…
OmniRouter: Budget and Performance Controllable Multi-LLM Routing
Kai Mei, Wujiang Xu, Minghao Guo +2
Large language models (LLMs) deliver superior performance but require substantial computational resources and operate with relatively low efficiency, while smaller models can effic…
iAgent: LLM Agent as a Shield between User and Recommender Systems
Wujiang Xu, Yunxiao Shi, Zujie Liang +6
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, th…
A-MEM: Agentic Memory for LLM Agents
Wujiang Xu, Zujie Liang, Kai Mei +3
While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current mem…
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding
Mingyu Jin, Kai Mei, Wujiang Xu +5
Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show that these concentrated massive values consistently emer…