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New SimWorld Environment Trains AI in Physical and Social Situations
Artificial intelligence can already solve math problems, write program code, and use a computer, but integrating it into complex physical and social realities remains challenging. The new SimWorld system aims to bridge this gap by providing a realistic virtual world where AI agents can practice operating in human-like environments.
SimWorld is built on the Unreal Engine 5 game engine and is specifically designed for large language models and vision-based AI models that can process both text and images. Researchers emphasize that if AI is expected to, for example, earn money, run a business, or otherwise 'succeed' in the real world, it must be able to practice in a vast array of different physical and social situations.
Current simulators offer poor starting points for this: they often rely on manually constructed, limited environments, game-like physics, and simplified social rules. Additionally, they usually do not provide direct support for the latest generation of language and vision models as AI agents.
SimWorld seeks to address these limitations by creating an open, highly realistic world where agents can move, perceive their environment, and interact with other actors. Such an environment provides a platform where AI can be both developed and evaluated in tasks that resemble real-life physical and social challenges.
According to researchers, large-scale, realistic simulation is a crucial step towards AI that not only solves abstract problems on a screen but also learns to operate in complex everyday situations.
Source: SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds, ArXiv (AI).
SimWorld is built on the Unreal Engine 5 game engine and is specifically designed for large language models and vision-based AI models that can process both text and images. Researchers emphasize that if AI is expected to, for example, earn money, run a business, or otherwise 'succeed' in the real world, it must be able to practice in a vast array of different physical and social situations.
Current simulators offer poor starting points for this: they often rely on manually constructed, limited environments, game-like physics, and simplified social rules. Additionally, they usually do not provide direct support for the latest generation of language and vision models as AI agents.
SimWorld seeks to address these limitations by creating an open, highly realistic world where agents can move, perceive their environment, and interact with other actors. Such an environment provides a platform where AI can be both developed and evaluated in tasks that resemble real-life physical and social challenges.
According to researchers, large-scale, realistic simulation is a crucial step towards AI that not only solves abstract problems on a screen but also learns to operate in complex everyday situations.
Source: SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds, ArXiv (AI).
This text was generated with AI assistance and may contain errors. Please verify details from the original source.
Original research: SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds
Publisher: ArXiv (AI)
Authors: Jiawei Ren, Yan Zhuang, Xiaokang Ye, Lingjun Mao, Xuhong He, Jianzhi Shen, Mrinaal Dogra, Yiming Liang, Ruixuan Zhang, Tianai Yue, Yiqing Yang, Eric Liu, Ryan Wu, Kevin Benavente, Rajiv Mandya Nagaraju, Muhammad Faayez, Xiyan Zhang, Dhruv Vivek Sharma, Xianrui Zhong, Ziqiao Ma, Tianmin Shu, Zhiting Hu, Lianhui Qin
December 23, 2025
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