A survey of large-model-based AI agents

Abstract Advances of large models (LMs) have catalyzed a paradigm shift in artificial intelligence, enabling the development of autonomous agents capable of complex reasoning, planning, and interaction with both digital and physical environments. As this field has expanded at an unprecedented rate, a comprehensive and structured overview is essential to consolidate current knowledge and guide future innovations. This survey addresses this need by providing a holistic re-view of LM-based artificial intelligence (AI) agents. First, we deconstruct the core architecture of modern LM-based agents and examine the interplay among key modules: Reasoning, perception, memory, planning, action, and learning. Subsequently, we systematically analyze the evaluation landscape, summarizing current benchmarks, metrics, and module-specific performance trade-offs. Furthermore, we sur-vey the transformative impact of these agents across a broad spectrum of applications, ranging from digital domains to embodied systems. The survey concludes by identifying critical challenges and future directions, thus offering a roadmap for the next generation of LM-based AI agents.