AI is moving into specialized domains such as pathology, meteorology, agriculture, and clinical or environmental benchmarks. Reproducibility and domain-specific evaluation are becoming central as generalist models show limits on complex real-world tasks.
Distribird is an agentic application that automates the construction of Bayesian priors from the literature, running entirely locally on open-weight models. Evaluated on 24 parameters across 10 domains, the multi-agent pipeline matches a single-promp...
A closed-loop system driven by an LLM that processes data from 49 phytosensors and directly controls lighting and microclimate autonomously optimized a vertical farm. The model cut the production cycle by 35% and uncovered an unlikely strategy – dark...
A dedicated benchmark tests 33 large language models on solid waste management tasks. The best model hits nearly 95% accuracy on easy questions, but on hard ones the average drops to 42.5%. Calculation, experimental design, and urban planning remain ...
An open model from DeepMind, published in Nature, improves cyclone forecasts by an extra day, but the real surprise is that it runs on a single NVIDIA H100. The code is on GitHub, marking a turning point for local inference of complex weather models.
Paige and Microsoft released PRISM2, a two-phase architecture that interprets whole-slide pathology images using clinical dialogue. Trained on over 2 million slides, it matches clinical-grade products for prostate and breast cancer. Weights are open,...
The new MS-MLB offers a shared, leakage-controlled pipeline to classify multiple sclerosis from blood RNA. Gradient Boosting leads with an AUC-ROC of 0.989, but the real value lies in the framework: reproducible, extendable, and built for research. A...
