DataEvolver: Let Your Data Build and Improve Itself via Goal-Driven Loop Agents
Constructing controllable visual data is a major bottleneck for image editing and multimodal understanding. Useful supervision is rarely produced by a single rendering pass; instead it emerges through iterative generation, inspection, correction, filtering, and export. We present DataEvolver, a closed-loop visual data engine that organizes this process around explicit goals, persistent artifacts, bounded corrective actions, and acceptance decisions. DataEvolver supports multiple artifact types, including RGB images, masks, depth maps, normal maps, meshes, poses, trajectories, and review traces. In the current release, the system operates through two coupled loops: generation-time self-cor...
| Authors | Qisong Zhang, Wenzhuo Wu, Zhuangzhuang Jia et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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