AdaCraft: Evolving Reliable Skills for Articulated 3D Generation

Generating a 3D articulated asset requires geometry that remains coherent as its parts move. Agents can repair individual construction programs, yet errors in joint frames, clearance and attachment recur across objects. Reusable skills could retain these corrections, but authoring and validating them requires expertise and repeated execution. This motivates automatically learning such guidance directly from execution feedback accumulated across generation attempts. However, automatically evolving skills introduces the challenge of stability and update adaptivity. Useful repairs must remain stable across later revisions, and the scope of each update must adapt to its effects on different mechanisms. We propose **AdaCraft**, a framework that addresses these challenges through Motion Memory and an Adaptive Edit Budget. Motion Memory retains geometric conditions and evaluated repairs, and uses a Homological Motion Signature to retain connection margins and bottleneck witnesses for required poses on a sampled motion graph. The Adaptive Edit Budget uses variation in functional improvements, execution uncertainty, geometric dependencies and motion fragility to control how many rules may change. Across 240 test tasks, AdaCraft achieves 75.1% functional yield compared with 58.1% for the strongest control, while reducing deployment calls per functional asset from 28.9 to 16.8.