Qing Guo

While Large Language Models (LLMs) enable complex autonomous behavior, current agents remain constrained by static, human-designed prompts that limit adaptability. Existing self-improving frameworks attempt to bridge this gap but typically rely on inefficient, multi-turn recursive loops that incu...

Advancing from usable to collaborative autonomy requires driving systems to execute passenger instructions safely and reliably. This work formulates instruction realization as scheduling across multiple motion planners and presents a dual-loop framework that provides a transparent decision chain...

Large Language Model-based Multi-Agent Systems represent a promising paradigm for tackling complex problems through agent collaboration. However, the reliance on open-ended communication exposes a fundamental vulnerability: the collaborative process itself can be exploited and disrupted. In this...