co-evolution training 1computer-use agents 1reinforcement learning 1stateful applications 1synthetic environments 1
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cs.AI2026
Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
Yash Pandya, Sahil Gupta, Sarthak Harne +10
Echoverse introduces a pipeline that compiles specifications into deep, stateful synthetic applications for training computer-use agents, using a co‑evolution loop that repairs env…
cs.AI2025
The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation
Tim R. Davidson, Adam Fourney, Saleema Amershi +3
The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with differen…
cs.AI2025
Magentic-UI: Towards Human-in-the-loop Agentic Systems
Hussein Mozannar, Gagan Bansal, Cheng Tan +17
AI agents powered by large language models are increasingly capable of autonomously completing complex, multi-step tasks using external tools. Yet, they still fall short of human-l…