[
    {
        "id": "osp-15399",
        "type": "article-journal",
        "title": "LeCuration: A Tiny World Model as a Data Curation Multi-Tool",
        "author": [
            {
                "family": "Sengupta",
                "given": "Mayank"
            },
            {
                "family": "Desai",
                "given": "Nirmit"
            },
            {
                "family": "Song",
                "given": "Eric"
            },
            {
                "family": "Sawarkar",
                "given": "Kunal"
            }
        ],
        "URL": "https://omanscience.com/en/articles/lecuration-a-tiny-world-model-as-a-data-curation-multi-tool",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]