How can AI see high-resolution images without drowning in pixels?

Multimodal AI models struggle with high-resolution images because standard approaches either miss details or waste computation on irrelevant patches. CVSearch intelligently switches between two strategies: first trying expert-guided visual search, then falling back to semantic-aware scanning that groups similar image regions together rather than rigid grid divisions. A complexity-driven bottom-up search then efficiently explores remaining details. On standard HR benchmarks, it matches best prior accuracy while cutting computational overhead substantially.