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Cosmos 3 sets a new benchmark for omnimodal models, outperforming existing state-of-the-art in both Text-to-Image and Image-to-Video tasks.
LLM-based recommendation systems can now dynamically adjust the granularity of knowledge graph retrieval, boosting performance by adapting to the complexity of user queries.
Stop hand-engineering your multi-agent LLM systems: UnityMAS-O lets you train them end-to-end with RL, unlocking surprisingly large gains, especially for smaller models.
Decomposing holistic visual cues into subtle, spatially-associated discrepancies allows for state-of-the-art ultra-fine-grained classification even with limited training data.
Soybean leaves have intricate vein structures that unlock state-of-the-art ultra-fine-grained visual categorization, even with limited data.
Unleashing multiple independently-optimized agents within a shared tree search dramatically boosts code generation performance, surpassing single-agent limitations.
