Multimodal intelligence can be regarded as a human‑guided co‑evolution between worlds and agents. Scaling this process encounters two bottlenecks: building heterogeneous, verifiable environments is expensive, and manual Harness iteration cannot keep up with the growing diversity and complexity of tasks. An important observation is that tasks with different modalities often share the same underlying capability requirements.\ \ We therefore introduce The Trinity, a recursive self‑improvement framework that couples world generation with Harness evolution through three roles: Creator, Explorer, and Evolver. Evolver reorganizes tasks from surface categories to shared requirements using verified execution trajectories, constructing a hierarchical capability tree.\ \ For heterogeneous tasks at the same capability node, Creator abstracts their common requirements into simplified, verifiable simulated worlds for capability learning, and then evaluates transfer on the original tasks. This process is called Capability‑Driven Real2Sim2Real.\ \ Along the capability tree, Explorer progressively evolves reusable capability modules via selective inheritance, forming a Harness tree whose validated updates accumulate across iterations within a fixed topology, with each branch specializing in a distinct capability. To quickly match suitable modules for new tasks, we design a Capability‑Aware Harness Router that selects compatible module configurations from the tree based on publicly available task information.\ \ Empirical results show that the evolved Harnesses improve mean performance by 34 points on 28 training benchmarks and by 26 points on 18 unseen benchmarks, and they successfully transfer to two real‑world embodied tasks.\ \ Review