发表机构
AIntropy AI（AIntropy AI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出LeCuration，一个基于LeWorldModel和扩散变换器的小世界模型，用于物理AI数据策展，通过嵌入和自回归预测实现异常检测与动作-状态一致性检查，并在CS:GO数据上进行了概念验证。
AI 中文摘要
物理人工智能的许多应用都在有限或封闭的物理世界中运行，这些世界由一组有限的物理定律支配着物体行为。例如，在仓库中工作的机器人和在视频游戏中移动的智能体。为了更好地组织、筛选和策展物理人工智能应用的数据，我们提出了一种新方法，该方法以各个数据集的独特设置和物理定律为中心。我们训练了LeCuration，这是一个小世界模型，旨在为另一个独立的、更大的下游模型充当数据策展工具。为了构建这个模型，我们选择LeWorldModel（LeWM）作为我们的潜在编码器和预测器，并添加了一个扩散变换器（DiT）解码器，以在自回归游戏玩法展开中增加视觉效果。我们发现，该模型的嵌入可以用作异常检测信号和基于内容的聚类启发式方法，并且通过该模型自回归地预测游戏状态，使我们能够定性检查动作-状态一致性。本文对CS:GO游戏数据进行了定性的概念验证案例研究；我们尚未报告定量策展指标或下游训练结果，我们将其确定为关键下一步。
英文摘要
Many applications of physical AI run within finite or closed physical worlds with a limited set of physical laws governing object behavior. Examples include robots working in a warehouse and agents moving around in a video game. In order to better organize, filter, and curate data for physical AI applications, we propose a new approach centered on the unique settings and physical laws of individual datasets. We train LeCuration, a small world model intended to serve as a data curation tool for a separate, larger downstream model. To build this model, we choose LeWorldModel (LeWM)as our latent encoder and predictor, adding a diffusion transformer (DiT) decoder to add visuals to autoregressive gameplay rollout. We find that the embeddings of this model can be used as an anomaly detection signal and as a content-based clustering heuristic, and that auto-regressively predicting the game state with this model allows us to qualitatively check for action-state consistency. This paper presents a qualitative, proof-of-concept case study on CS:GO gameplay data; we do not yet report quantitative curation metrics or downstream training results, which we identify as the key next step.
Comments9 pages
