Xinyu Zhao

Multimodal Large Language Models (MLLMs) show strong potential for cross-modal understanding by integrating powerful language models with multimodal encoders. However, extending MLLMs to handle a diverse range of modalities introduces two critical and intertwined challenges: (1) the reliance on f...

While memory is a core component in agent systems, its behavioral impact in complex, long-horizon domains like machine learning engineering (MLE) remains poorly understood. Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors...

Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors: a single faulty step can propagate across agents and disrupt the trajectory. In this paper, we present MASC, a metacognitive framework that endows MAS with real-time...