arxiv6d agobullish
arXiv:2606.05688v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale foundation models efficiently by activating only a subset of experts for each token, but their large number of expert parameters still makes quantization essential for practical deployment. Unlike dense models, how
arxivMay 22bullish
arXiv:2602.04768v2 Announce Type: replace Abstract: Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, extending this paradigm to general, real-world graphs is challenging. I
arxivApr 27
arXiv:2604.22348v1 Announce Type: new Abstract: Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-scale unlabeled healthcare records. In natural language processing, scali
arxivApr 16bullish
arXiv:2510.00919v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for expert-level reasoning-such as solving Olympiad-level physics problems-remains largely unexplored. Inspir
arxivApr 10bullish
arXiv:2508.20765v2 Announce Type: replace-cross Abstract: The automatic understanding of video content is advancing rapidly. Empowered by deeper neural networks and large datasets, machines are increasingly capable of understanding what is concretely visible in video frames, whether it be objects, a