LLM News

Researchers have developed RDB-PFN, the first foundation model designed specifically for relational databases, trained entirely on synthetic data to overcome the scarcity of high-quality private databases. Pre-trained on over 2 million synthetic relational and single-table tasks, the model achieves few-shot performance on 19 real-world relational prediction tasks while outperforming existing graph-based and single-table baselines.

Researchers at Stanford have released Merlin, a 3D vision-language model designed specifically for abdominal CT scan interpretation. Trained on 6+ million CT images, 1.8 million diagnosis codes, and 6+ million report tokens from 15,331 scans, Merlin outperforms 2D medical vision-language models on diagnostic classification, phenotyping, and semantic segmentation across internal and external validation sets.