AGIBOT has unveiled GO-3 Preview, an embodied foundation model that supports large-scale learning across the full lifecycle of pre-training, post-training and post-deployment. The model has secured first-place rankings on three major embodied AI benchmarks – RoboDojo, RoboTwin and RoboColiseum – and will be fully open-sourced by the end of 2026. Built on AGIBOT’s proprietary embodied-native architecture, WRAM (World Reasoning Action Model), GO-3 Preview unifies task understanding, future-state prediction and action generation to enable robots to generalize across tasks and perform precise manipulation. Together with Genie Evolver, AGIBOT’s hybrid post-training system, and Genie Master, its embodied agent framework, GO-3 Preview forms an integrated learning system spanning pre-training, post-training and real-world deployment. AGIBOT calls this approach “full-lifecycle scaling”, designed to help robots develop general capabilities, refine their skills through interaction and continuously improve by learning from real-world work. The company said that for embodied AI, the challenge is not simply to scale model training, but to extend learning across the entire lifecycle, with robots learning through post-training interactions and continuing to adapt after deployment. During pre-training, GO-3 Preview addresses variation by scaling both data and model capabilities through its embodied-native WRAM architecture, designed to help robots learn transferable skills and adapt more quickly to unfamiliar situations.