LeCuration：作为数据整理多工具的小型世界模型

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Abstract: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.

Comments:	9 pages
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
MSC classes:	68T07, 68T40, 68T45
ACM classes:	I.2.10; H.3.3; I.4.8
Cite as:	arXiv:2610.09285 [cs.LG]
	(or arXiv:2610.09285v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.09285 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nirmit Desai [view email]
[v1] Wed, 7 Oct 2026 01:41:23 UTC (3,588 KB)