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A 0.9B parameter omni-modal embedder achieves state-of-the-art text retrieval quality by distilling a frozen text backbone's embeddings into new modalities, bypassing the common modality-extension performance drop.
Open-ended reinforcement learning with LLM-based rewards unlocks surprisingly strong performance in medical reasoning for multimodal models, even with limited training data.
