Imagine a world where AI can build structures not just in virtual reality, but translate textual descriptions into physical layouts. That's the intriguing question researchers tackled in a new study exploring whether Large Language Models (LLMs) can reconstruct and simulate the physical world using only the knowledge they've absorbed from text. The physical world, with its complex geometry and spatial constraints, presents a significant challenge for LLMs. This research explores how these AI models can understand and generate 3D spatial representations solely from text descriptions. The researchers designed a system that simplifies 3D geometry into basic cube combinations. They then investigated how LLMs can perform multi-step geometric inferences within a spatial environment using multi-layer graphs and a standardized set of geometric conventions. Think of it like giving the LLM a rulebook for understanding spatial relationships. They even used a genetic algorithm inspired by LLM knowledge to solve geometric constraint problems. Essentially, the AI learns to optimize the placement of objects by iteratively refining its solutions. Comparing the performance of GPT-3.5-turbo and GPT-4, the study found that GPT-4 excelled in spatial construction tasks thanks to its superior performance. However, even GPT-4 relied on simple blocks, highlighting the continued challenges LLMs face in understanding nuanced spatial details. This research represents a fascinating early step towards using text-based LLMs as physical world builders. While there are limitations, such as the current reliance on simplified shapes and the computational resources required, the potential applications are immense. Imagine AI designing buildings, optimizing factory layouts, or even creating personalized furniture based solely on your textual description. This study opens doors to a future where the line between the digital and physical worlds blurs even further, with LLMs playing a key role in shaping our physical environment.