AI RESEARCH
X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling
arXiv CS.CV
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ArXi:2605.24892v2 Announce Type: replace Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and generalizable planning. Predictive world modeling enables VLA to internalize physical dynamics and long-term causality by predicting future video from past observations. However, naive next-frame prediction faces two challenges: 1) unlike semantically distinct text tokens, video tokens are low-entropy and redundant, causing prediction to degenerate into trivial extrapolation.