# DexForesight

DexForesight injects predictive features from an action-conditioned spatial world model into a dexterous policy's latent space through query alignment, and at deployment keeps only the policy, which acts from RGB, language and robot state. On 11 DexJoCo simulation tasks it raises the GR00T N1.7 backbone's overall success from 49.4% to 60.5%, and on three real-robot tasks it reaches 58.3%, 8.3 points above the strongest baseline. The world model, depth encoder and future-prediction heads are all removed at inference.

DexForesight injects predictive features from an action-conditioned spatial world model into a dexterous policy's latent space through query alignment, and at deployment keeps only the policy, which acts from RGB, language and robot state. On 11 DexJoCo simulation tasks it raises the GR00T N1.7 backbone's overall success from 49.4% to 60.5%, and on three real-robot tasks it reaches 58.3%, 8.3 points above the strongest baseline. The world model, depth encoder and future-prediction heads are all removed at inference.