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cs.AI2026
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
Yuheng Jing, Kai Li, Ziwen Zhang +8
In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with un…
cs.AI2026
K^2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control
Zhe Wu, Donglin Mo, Hongjin Lu +7
Existing mobile device control agents often perform poorly when solving complex tasks requiring long-horizon planning and precise operations, typically due to a lack of relevant ta…
cs.AI2025
Hi-Agent: Hierarchical Vision-Language Agents for Mobile Device Control
Zhe Wu, Hongjin Lu, Junliang Xing +10
Building agents that autonomously operate mobile devices has attracted increasing attention. While Vision-Language Models (VLMs) show promise, most existing approaches rely on dire…