Small LLMs close gap via iterative scene search

The gap between frontier and small models may be narrower than single-shot benchmarks suggest — self-correction loops could be the great equalizer.

• –The core insight: smaller models often *recognize* failure even when they can't solve the problem directly, making them viable for search-based approaches
• –Combining task decomposition with visual feedback (render → screenshot → compare) creates a tight eval loop applicable beyond 3D scenes
• –References Karpathy's autosearch concept as a natural extension — verifiable outputs are exactly where iterative search shines
• –Practical implication: on-device or low-cost models running more inference steps could rival expensive one-shot calls for structured generation tasks
• –Low engagement (score: 6, 5 comments) but the idea is substantive enough to track as the benchmark space matures