Large language models (LLMs) are increasingly used in software development, but their level of software security expertise remains unclear. This work systematic...
Large language models (LLMs) have revolutionized software development through AI-assisted coding tools, enabling developers with limited programming expertise t...
Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the da...
We demonstrate how to formally specify distributed algorithms as declarative axiomatic theories in a modal logic. We exhibit the method on a simple voting proto...
Large language models (LLMs) are increasingly deployed as conversational assistants in open-domain, multi-turn settings, where users often provide incomplete or...
Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability to understa...
Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL)...
Large Language Models (LLMs) are widely used for automated code generation, yet their apparent successes often mask a tension between pretraining objectives and...
Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as tria...
Distributed attention is a fundamental problem for scaling context window for Large Language Models (LLMs). The state-of-the-art method, Ring-Attention, suffers...
As foundation models grow in size, fine-tuning them becomes increasingly expensive. While GPU spot instances offer a low-cost alternative to on-demand resources...
Locating the files and functions requiring modification in large open-source software (OSS) repositories is challenging due to their scale and structural comple...
