Transformers are being deployed across medicine, chemistry, biology and space, with domain-specific data and multimodal disagreement becoming central. These applications favor specialized on-board models over general-purpose chat.
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...
AstroForge will give its next Autonomy-1 space probe a small transformer-based model responsible for onboard decisions. Local inference becomes unavoidable without connectivity and under severe physical constraints. The shift from deterministic softw...
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.
MDT turns divergence among face, voice, and text into the central signal for clinical analysis, reaching 0.7408 Macro F1 on the BAH test and training in less than twenty minutes on a single GPU. For AI-Radar the key insight is operational: a model en...
MDT turns contradictions between face, voice, and language into an explicit signal. With nine tokens, FiLM modulation, and LoRA fine-tuning, it reaches 0.7408 Macro F1 on the BAH test split and 0.7368 on the private leaderboard, outperforming the str...
