6 accepted papers
LLMs have traditionally scaled along dense dimensions, where performance is coupled with near-linear increases in computational cost. While MoE decouples capacity from compute, it introduces large memory overhead and hardware efficiency challenges. To overcome these, we propose token-indexed paramet…
Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, particularly when directly employing LLMs as an intervention doctor. Researchers have mainly focused on using prompt engineering for role play as an intervention doctor and integrating a
Molecular pretrained representations (MPR) has emerged as a powerful approach for addressing the challenge of limited supervised data in applications such as drug discovery and material design. While early MPR methods relied on 1D sequences and 2D graphs, recent advancements have incorporated 3D co…
In recent decades, chemistry publications and patents have increased rapidly. A significant portion of key information is embedded in molecular structure figures, complicating large-scale literature searches and limiting the application of large language models in fields such as biology, chemistry,…
Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handli…
Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two non-human primates (NHPs) as they per…