Self-Supervised Multi-Modal World Model with 4D Space-Time Embedding

Lance Legel; Qin Huang; Brandon Voelker; Daniel Neamati; Patrick Alan Johnson; Favyen Bastani; Jeff Rose; James Ryan Hennessy; Robert Guralnick; Douglas Soltis; Pamela Soltis; Shaowen Wang

arXiv · 2603.07039

Self-Supervised Multi-Modal World Model with 4D Space-Time Embedding

Lance Legel·Qin Huang·Brandon Voelker·Daniel Neamati·Patrick Alan Johnson·Favyen Bastani·Jeff Rose·James Ryan Hennessy·Robert Guralnick·Douglas Soltis·Pamela Soltis·Shaowen Wang

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Abstract

We present DeepEarth, a self-supervised multi-modal world model with Earth4D, a novel planetary-scale 4D space-time positional encoder. Earth4D extends 3D multi-resolution hash encoding to include time, efficiently scaling across the planet over centuries with sub-meter, sub-second precision. Multi-modal encoders (e.g. vision-language models) are fused with Earth4D embeddings and trained via masked reconstruction. We demonstrate Earth4D's expressive power by achieving state-of-the-art performance on an ecological forecasting benchmark. Earth4D with learnable hash probing surpasses a multi-modal foundation model pre-trained on substantially more data. Access open source code and download models at: https://github.com/legel/deepearth

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Lance Legel, Qin Huang, Brandon Voelker, Daniel Neamati, Patrick Alan Johnson, Favyen Bastani, Jeff Rose, James Ryan Hennessy, Robert Guralnick, Douglas Soltis, Pamela Soltis, Shaowen Wang. 2026-03-07. Self-Supervised Multi-Modal World Model with 4D Space-Time Embedding. https://arxiv.org/abs/2603.07039

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