With the continuous maturity of deep learning technologies, the accumulation of big data resources, and the explosive growth of computational performance, the field of Artificial Intelligence (AI) has gradually stepped into the “new frontier” of cognitive intelligence after nearly seven decades of development. Since 2023, large language models (LLMs)-driven general intelligence has become the focus of global AI research, showing great differences from previous specialized intelligence systems. However, there is still a lack of in-depth and systematic exploration of the technical characteristics, intelligence features, and thinking patterns of LLMs, and the reliability issues of LLMs also restrict their application in various scenarios.
Therefore, Zhidong CAO, Xiangyu ZHANG, and Daniel Dajun ZENG from the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, and the School of Artificial Intelligence, University of Chinese Academy of Sciences have jointly conducted a research entitled “Large Language Models: Technology, Intelligence, and Thought”. This work was supported by the National Natural Science Foundation of China (Grant Nos. 72025404 and 72293575).
This study comprehensively explores the technical characteristics, intelligence features, and thinking patterns of LLMs, and clarifies their position and role in the field of general intelligence through comparative analysis with traditional models.
The research results show that LLMs are a new type of technological species in the field of general intelligence. Their problem-solving method is based on empirical learning from observed data rather than “first principles” (logocentrism). Technologically, LLMs have common sense, deep reasoning, strong generalization, and natural human-computer interaction capabilities; in terms of intelligence, they show memory-driven core characteristics, powerful data-driven learning capabilities, and excellent generalization abilities; in terms of thought, they have highly human-like cognitive traits such as contextual understanding, analogy, and intuitive reasoning. These capabilities enable LLMs to adapt to a variety of complex and open scenarios, which is in sharp contrast to traditional models that focus on formal logic, quantitative analysis, and narrow problem structures. The emergence of LLMs is expected to promote major changes in AI theory and applications, and may redefine the way intelligent systems conduct decision-making, strategic reasoning, and contextual understanding in uncertain and dynamic environments.
The paper “Large Language Models: Technology, Intelligence, and Thought” authored by Zhidong CAO, Xiangyu ZHANG, Daniel Dajun ZENG was published in Front. Eng. Manag. 2025, 12(3): 710–715. The full text can be accessed via https://doi.org/10.1007/s42524-025-5004-3.