Industrial recommender systems face two fundamental limitations under the log-driven paradigm: (1) knowledge poverty in ID-based item representations that cause...
The increasing integration of AI tools in education has led prior research to explore their impact on learning processes. Nevertheless, most existing studies fo...
Methods that use Large Language Models (LLM) as planners for embodied instruction following tasks have become widespread. To successfully complete tasks, the LL...
In hard-label black-box adversarial attacks, where only the top-1 predicted label is accessible, the prohibitive query complexity poses a major obstacle to prac...
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...
