3 citations · 5 across the 9 of their papers we have counts for
9 papers · 1 filter
LEASH: Uncertainty-Aware Weak-to-Strong Escalation and Oversight for Computer-Use Agents
Qingni Wang, Changdae Oh, Yuzhe Yang +4
Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails. Weak-to-stron…
AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
Xin Wang, Wenhao Wu, Menghao Zhang +3
Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contribution of individual decisions. St…
Absorbing State Phase Transitions in Multi-Agent Search
Wenwen Zheng, Yuzhe Yang, Helen Qu +2
Nontrivial dynamics can emerge in large language model (LLM)-based multi-agent systems, and preliminary evidence exists that formalisms from statistical mechanics can be effective…
Neuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution
Hyewon Suh, Thanh Minh Nguyen, Chih-Lun Lee +5
Many computer tasks recur: the same workflow runs many times, with new inputs and from different starting states. Current computer-use agents re-plan every step of every run, which…
HarnessPAI: An Evolving Harness for Physical AI
Xin Wang, Wenhao Wu, Menghao Zhang +20
Physical AI aims to build embodied agents that perceive the world, understand and reason about it, and decide how to act. Yet the field has focused primarily on the last component:…
TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents
Wenhao Wu, Menghao Zhang, Xin Wang +3
Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability but on consistency and limit-awareness: behaving the same way across repeated tria…
Do Multi-Agent LLMs Explore Each Other?
Hyeong Kyu Choi, Jiatong Li, Wendi Li +2
Exploration is essential for reliable autonomy in multi-agent systems, yet it remains unclear whether large language model (LLM) agents can explore effectively when interacting wit…
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…
On the Reliability of Computer Use Agents
Gonzalo Gonzalez-Pumariega, Saaket Agashe, Jiachen Yang +2
Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet…