Core Positioning and Problem Solving - The article discusses the need for a realistic and interactive environment to rigorously evaluate the performance of large language models (LLMs) in embodied scenarios, highlighting limitations of existing simulators [2] - The proposed solution is VirtualEnv, a next-generation simulation platform based on Unreal Engine 5, aimed at supporting language-driven, multimodal interactions for embodied AI research [2] Related Work and Platform Advantages - VirtualEnv integrates multidimensional capabilities, surpassing existing platforms in terms of environment type, task scale, and action space [3] - It supports 3D multi-room and indoor-outdoor environments, with 140,000 unique tasks across various categories, enhancing the complexity and applicability of AI research [5] Core Functionality Design - The platform's architecture is built on three core pillars, enabling support for complex scenarios and high-level reasoning tasks [4] - It features high-fidelity rendering and over 20,000 interactive assets, allowing for detailed object manipulation and realistic interaction feedback [9] Language-Driven Interaction and Scene Generation - VirtualEnv natively supports integration with LLMs and visual language models (VLMs), enabling automatic scene generation based on natural language commands [6][8] - The platform allows for dynamic modifications of the environment through natural language instructions, ensuring precise adjustments without manual intervention [8] Scene Graph Representation - A hierarchical scene graph organizes the environment, encoding objects, agents, and spatial relationships, facilitating complex reasoning tasks [11] Experimental Validation and Key Findings - In a blind test, VirtualEnv achieved a visual realism score of 4.46±1.02, significantly higher than other platforms, validating its advantages in environmental realism [12] LLM Performance Comparison - The article compares reasoning LLMs with non-reasoning LLMs across various tasks, revealing that reasoning models outperform non-reasoning ones, particularly in complex multi-step tasks [15] Failure Mode Analysis - Six major failure modes were identified, with reasoning LLMs showing an average task completion rate improvement of 11% in complex tasks, indicating the importance of structured reasoning [16][21] Summary and Value - VirtualEnv is positioned as a high-fidelity, interactive, multimodal simulation platform that could accelerate the application of LLMs in real-world interactive scenarios, supporting various applications in interactive entertainment and robotic navigation [20]