C-World: A Computer Use Agent Environment Creator
Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou

TL;DR
C-World is a versatile environment creation system that enables scalable, on-demand generation of agent environments for improved planning and reasoning in large language models.
Contribution
It introduces a comprehensive environment creation framework with both realistic and synthesized modes, facilitating scalable testing and training of LLM-based agents.
Findings
Planning ability is strong across state-of-the-art LLMs.
Execution remains the main bottleneck in agent performance.
C-World's World Engine correlates highly with real execution and outperforms baselines with limited data.
Abstract
To close the gap between LLM-based agents and humans in planning and reasoning, agents need large-scale, diverse environments for continuous learning -- yet building such environments is itself prohibitively expensive. We present C-World, an environment creation system that enables users to build agent environments on demand. We define a complete agent environment through four components: an Action Space of 5,571 format-unified tools across 204 common applications, a Task Distribution engine that synthesizes long-horizon workflows with wild constraints, a Transition Function implemented as a state controller that injects realistic failures and perturbations, and a Reward Signal combining verifiable metrics with LLM-based judgment. C-World operates in two modes: a realistic mode grounded in live API execution, and a synthesized mode powered by the World Engine, which approximates tool…
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