From the 1 of 7 linked papers with an AI index.
7 papers
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33
The paper presents OSWorld 2.0, a benchmark consisting of 108 long‑horizon, real‑world computer‑use workflows designed to evaluate how well AI agents can handle complex, multi‑step…
EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents
Gurusha Juneja, Dylan Lu, Saaket Agashe +7
Theory of Mind (ToM), the ability to track others epistemic state, makes humans efficient collaborators. AI agents need the same capacity in multi agent settings, yet existing benc…
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…
Context Bootstrapped Reinforcement Learning
Saaket Agashe, Jayanth Srinivasa, Gaowen Liu +2
Reinforcement Learning from Verifiable Rewards (RLVR) suffers from exploration inefficiency, where models struggle to generate successful rollouts, resulting in minimal learning si…
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models
Saaket Agashe, Yue Fan, Anthony Reyna +1
Large Language Models (LLMs) have demonstrated emergent common-sense reasoning and Theory of Mind (ToM) capabilities, making them promising candidates for developing coordination a…
Self-Resource Allocation in Multi-Agent LLM Systems
Alfonso Amayuelas, Jingbo Yang, Saaket Agashe +4
With the development of LLMs as agents, there is a growing interest in connecting multiple agents into multi-agent systems to solve tasks concurrently, focusing on their role in ta…