Smaller specialized models and synthetic data are advancing biomedical and physical AI. Clinical toxicity, retrosynthesis, 3D cell reconstruction and physical AI edge cases show a push toward explainable, domain-aware systems.
A 0.4M-parameter multimodal model combines SMILES transformers and GATv2 graphs via cross-attention, reaching AUC-ROC 0.987 and F1 0.906 on ClinTox. The ChemBERTa-2-backed variant posts a mean AUC-ROC of 0.750 on Tox21, comparable to larger baselines...
Hungarian startup DiffuseDrive generates synthetic data to train physical AI systems in defence, mining, aerospace and autonomous vehicles. Its platform identifies missing edge cases in datasets, produces them with control over object position and si...
A population-supervised framework links 2D red-cell images to latent biophysical quantities, aggregating them into mean corpuscular volume, red-cell distribution width, and mean corpuscular haemoglobin. It combines shared local inference, a structure...
A token-matched study shows didactic and clinical data shape medical large language models asymmetrically: clinical data improves clinic-oriented tasks while remaining competitive on knowledge-intensive ones, whereas didactic data mainly boosts theor...
Microsoft Research publishes in Nature a framework combining a Transformer model and a graph neural network. Learned ranking reduces unconstrained proposals and outperforms previous models in tests with chemists. Code and weights are open source.
