BlendCUA: Self-Evolving Computer-Use Harness for Blender Use

This paper introduces BlendCUA, a self-evolving method for harness generation for an LLM agent carrying out Blender tasks given in natural language with reference imagery, covering modelling, materials, physics simulation and animation. Such an agent depends on its harness, the tools it can use and the skills that show it procedures, and building one by hand for every new family of tasks costs as much as the expert demonstrations it was meant to replace. Self-improving agents grow a harness by compiling successful trajectories into skills, which captures what the agent did rather than what its result got wrong. We instead evolve the harness from the defects in the agent's own finished work, through outcome-driven credit assignment. Starting from a hand-built harness, BlendCUA builds each task, has an independent checker name the defects in the finished model, traces each defect to the part of the harness responsible, and keeps a repair to that part, whether a plan correction, a skill or a tool, only if a rebuild shows the defect closed. Evaluated on held-out Blender tasks with judged and computed measures of quality, BlendCUA outperforms a harness learned from transcripts on every measure under the same agent and curriculum, reducing major defects by 29%. These results establish that the harness an agent needs for professional creative software can be grown from its own finished work rather than authored by an expert.