We use scaling law derivation to compare open language-vision foundation models (CLIP, MaMMUT) and datasets (DataComp-1.4B, Re-LAION-1.4B, DFN-1.4B), identifying models and datasets that promise stronger scalability in the pre-training.

This paper demonstrates that low precision causes non-reproducible LLM inference across different setups, proposing a hybrid-precision method, LayerCast, that computes in FP32 to achieve determinism while saving memory.

Explicit clustering bias added during training improves structural consistency of cell embeddings but does not reveal clear cell types in mouse V1

ClinBench is an open-source, multi-model, multi-domain framework for rigorously benchmarking large language models on clinical information-extraction tasks.

We provide a statistically rigorous guidelines for training interactive, multi-step LLM agents, exploring optimal compute allocation, generalization, and hyperparameter settings.