In this paper, we focus on the \emph{virtual world}, a cyberspace where people can live in. An ideal virtual world shares great similarity with our real world. One of the crucial aspects is its evolving nature, reflected by individuals' capability to grow and thereby influence the objective world. Such dynamics is unpredictable and beyond the reach of existing systems. For this, we propose a special engine called \textbf{\emph{Delta-Engine}} to drive this virtual world. $\Delta$ associates the world's evolution to the engine's scalability. It consists of a base engine and a neural proxy. The base engine programs the prototype of the virtual world; given a trigger, the neural proxy generates new snippets on the base engine through \emph{incremental prediction}. This paper presents a full-stack introduction to the delta-engine. The key feature of the delta-engine is its scalability to unknown elements within the world, Technically, it derives from the prefect co-work of the neural proxy and the base engine, and the alignment with high-quality data. We introduce an engine-oriented fine-tuning method that embeds the base engine into the proxy. We then discuss the human-LLM collaborative design to produce novel and interesting data efficiently. Eventually, we propose three evaluation principles to comprehensively assess the performance of a delta engine: naive evaluation, incremental evaluation, and adversarial evaluation.

翻译：本文聚焦于\emph{虚拟世界}——一个可供人类栖居的赛博空间。理想的虚拟世界与现实世界高度相似，其关键特征之一在于其动态演化性，体现为个体能够成长并进而影响客观世界。这种动态性是不可预测的，且超出了现有系统的能力范围。为此，我们提出一种名为\textbf{\emph{Delta-Engine}}的特殊引擎来驱动该虚拟世界。$\Delta$将世界的演化与引擎的可扩展性相关联。该引擎由基础引擎与神经代理两部分构成：基础引擎对虚拟世界原型进行编程；当触发条件满足时，神经代理通过\emph{增量预测}在基础引擎上生成新的片段。本文对delta-engine进行了全栈式介绍。该引擎的核心特征在于其对世界内未知元素的可扩展性，其技术根源在于神经代理与基础引擎的完美协同工作，以及与高质量数据的对齐。我们提出一种面向引擎的微调方法，将基础引擎嵌入至代理中。随后探讨人机协作设计模式，以高效生成新颖有趣的数据。最后，我们提出三项评估原则来全面衡量delta引擎的性能：基础评估、增量评估与对抗性评估。