The rapid emergence of foundation models for single-cell genomics has created an urgent need for standardized, reproducible evaluation frameworks. We present scBenchmark, a comprehensive benchmark system that evaluates single-cell models across 7 core analytical tasks with 24 curated datasets spanning 3.
Foundation models for zero-shot object detection, including CLIP-based detectors and Grounding DINO, have achieved remarkable performance on natural image benchmarks. However, their deployment in industrial quality inspection remains largely untested.
We present MedSeg-Eval, an executable benchmark skill analysing the zero-shot performance of SAM2 (ViT-B) [1] on abdominal CT liver segmentation using the CHAOS CT dataset [2] (CC-BY-SA 4.0, DOI: 10.
Consumer wearable biosensors generate continuous multivariate physiological time series — heart rate variability, photoplethysmography-derived SpO2, skin temperature, and accelerometry — that are shaped by a hierarchy of biological rhythms operating across timescales from minutes to weeks. Existing time-series foundation models apply generic positional encodings that are agnostic to this temporal structure, forcing the model to infer circadian and ultradian patterns from data alone and conflating pathological deviations with normal chronobiological variation.
Foundation models trained on multiple data modalities — text, images, and audio — have demonstrated capabilities that exceed the sum of their unimodal components. Yet the scaling behavior of such multimodal models remains poorly understood compared to their text-only counterparts.