LLM research → agent research. As mentioned before, AI agents need to be able to perceive
the environment, make decisions, and execute appropriate actions [4; 9]. Among the critical steps,
understanding the content input to the agent, reasoning, planning, making accurate decisions, and
translating them into executable atomic action sequences to achieve the ultimate goal is paramount.
Many current endeavors utilize LLMs as the cognitive core of AI agents, and the evolution of these
models provides a quality assurance for accomplishing this step [22; 114; 115; 410].
With their robust capabilities in language and intent comprehension, reasoning, memory, and even
empathy, large language models can excel in decision-making and planning, as demonstrated before.
Coupled with pre-trained knowledge, they can create coherent action sequences that can be executed
effectively [183; 258; 355]. Additionally, through the mechanism of reflection [169; 178], these
TheRiseandPotentialofLargeLanguageModel BasedAgents