DeepSeek's new AI model uses a unified multimodal learner for clinical prediction, simplifying the process and achieving state-of-the-art results. This approach converts all patient data into a single natural language sequence and fine-tunes a pretrained language model. The model outperforms task-specific multimodal baselines and a clinically deployed gradient boosting system.

DeepSeek has unveiled a groundbreaking approach to abstract reasoning on ARC-AGI-1, leveraging an open-weight model (DeepSeek V3.2) in a 'non-thinking' mode, augmented by innovative agentic harnesses. This method achieves impressive generalization and pattern discovery, reaching up to 67.25% pass@2 with unprecedented cost-efficiency, sidestepping heavy compute or benchmark-specific fine-tuning.