[R] Large scale evals for multimodal composed search

Meta AI has introduced SEAL (Search Engine with Autoregressive LLMs), a groundbreaking benchmark dataset designed to evaluate multimodal composed search systems. The dataset contains 1.3 million diverse queries that combine text, images, and complex compositional reasoning, creating a comprehensive testing ground for AI search technologies. This represents a significant advancement in evaluation methodology, moving beyond simple keyword matching to assess how well systems can understand and execute multi-step search instructions across different modalities.

For researchers and developers, SEAL provides standardized metrics to compare different approaches to multimodal search. The benchmark tests capabilities like visual question answering, compositional reasoning (combining multiple concepts), and cross-modal understanding. By offering this large-scale evaluation resource, Meta is lowering barriers for smaller research teams who previously lacked the resources to create comprehensive test sets, accelerating innovation in AI-powered search technologies.