Manifold AI's manifold space has claimed the championship position in the Agent Track of the RoboDojo benchmark for robotic learning, outperforming models like GPT-6-Astra and NVIDIA GR00T-N1.7. Its success is primarily due to two cutting-edge technologies: Firstly, the WorldScape Policy technology, which boasts contextual learning capabilities, enables robots to acquire new tasks without the need for fine-tuning. A single human demonstration is sufficient for the robots to achieve action transfer, skill composition, and zero-shot learning for long-term tasks. Secondly, the robotic recursive self-improvement framework, WorldScape R²SI, seamlessly integrates task execution, experience review, and skill updates through an inner-outer dual-agent system. This integration allows robots to continuously accumulate experience and enhance their capabilities over multiple executions. Together, these two technologies enable robots to more effectively utilize their existing capabilities to adapt to various changes. This victory marks a significant milestone in Manifold AI's ongoing series of explorations, with the technical pathway set to undergo further validation in real-world application scenarios.