AI models are being applied to drug identification, oncology prognosis, driver identification, and mathematical benchmarks, moving beyond generic chat and coding. These specialized tasks show credible results but also fragile rankings and evaluation challenges.
TEMPEST uses a Temporal Convolutional Network with ArcFace loss to produce angular embeddings from 60-second multimodal driving windows. On 45 drivers it reaches 91.71% Rank-1 accuracy, outperforming the best classical model by 17.9 points and the st...
At the Heidelberg Laureate Forum, OpenAI’s Navier–Stokes claim ignited debate. AI companies are turning math into a benchmark, while academics report pressure, costs, and verification shifting outside peer control. The split is not only about theorem...
A new framework, SSU-LSF, introduces machine unlearning for Mamba-family State Space models used in land surface forecasting. It localizes the temporal footprint of confounders with EKFac functions, spectral-radius-weighted thresholding, and projecte...
A study proposes HyMLRaman, a hybrid framework combining deep feature extraction, generative models, and classical classifiers to identify six pharmaceutical compounds via Raman spectroscopy. The EfficientNet-B3--SVM pair reaches 96.31% accuracy. Gen...
The Survprompt framework turns structured clinical data into vignettes and asks pre-trained LLMs to estimate survival without fine-tuning. Across multi-institutional cohorts, frontier models reached cMAE competitive with random survival forests in se...
