activity
20242026
most citedSelf-Resource Allocation in Multi-Agent LLM Systems

3 citations · 5 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

Scaling Agents for Computer Use

Gonzalo Gonzalez-Pumariega, Vincent Tu, Chih-Lun Lee +3

Computer-use agents (CUAs) hold promise for automating everyday digital tasks, but their performance on long-horizon, complex problems remains unreliable. Single-rollout execution…

cs.AI2025

Agents of Change: Self-Evolving LLM Agents for Strategic Planning

Nikolas Belle, Dakota Barnes, Alfonso Amayuelas +3

We address the long-horizon gap in large language model (LLM) agents by enabling them to sustain coherent strategies in adversarial, stochastic environments. Settlers of Catan prov…

cs.AI2025

Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

Saaket Agashe, Kyle Wong, Vincent Tu +3

Computer use agents automate digital tasks by directly interacting with graphical user interfaces (GUIs) on computers and mobile devices, offering significant potential to enhance…

cs.AI2024★ 2 cited

Agent S: An Open Agentic Framework that Uses Computers Like a Human

Saaket Agashe, Jiuzhou Han, Shuyu Gan +3

We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer inte…