See, Point, Refine: Multi-Turn Approach to GUI Grounding with Visual Feedback

The project addresses a critical bottleneck in Computer Use Agents (CUAs): the 'fat-finger' problem where standard VLMs fail to interact with tiny, dense UI elements in professional software like VS Code. By introducing a multi-turn refinement loop (See, Point, Refine), it moves beyond single-shot coordinate prediction. However, the project's defensibility is low (score 3) because it is a research-centric implementation with zero current adoption (0 stars) and the technique is likely to be subsumed by frontier labs. Anthropic's 'Computer Use' and Microsoft's 'Windows Agent' efforts are already experimenting with similar zoom-in and iterative correction mechanisms. The 4 forks within 3 days suggest some academic interest, but no ecosystem moat exists. The displacement horizon is very short (6 months) as the next generation of models (GPT-5, Claude 4) will likely integrate visual-spatial reasoning directly into the latent space or through native recursive sampling, making this specific algorithmic wrapper obsolete.