28 citations · 37 across the 7 of their papers we have counts for
7 papers
ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents
Tianchen Guan, Xinlei Lin, Royce Cheng-Yue +2
Current evaluation of computer-use agents is split between long-horizon workflow benchmarks and atomic GUI-grounding tests. This leaves an under-instrumented middle layer: realisti…
QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
Jian Xie, Tianhe Lin, Zilu Wang +16
Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information.…
Recursive Agent Optimization
Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang +2
We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and delegate sub-tasks to new instantiations…
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
Ziluo Ding, Wanpeng Zhang, Junpeng Yue +3
We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also…
WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments
Xi Chen, Tianyu Shi, Qingpeng Zhao +3
Recent advances in deep reinforcement learning (RL) have demonstrated complex decision-making capabilities in simulation environments such as Arcade Learning Environment, MuJoCo, a…
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Kefan Su, Siyuan Zhou, Jiechuan Jiang +3
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL). However, non-stationarity remains a significant challenge in fully decentr…
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Xiangjun Wang, Junxiao Song, Penghui Qi +9
AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy…