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相关论文: The Rise and Potential of Large Language Model Bas…

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Intelligent agents stand out as a potential path toward artificial general intelligence (AGI). Thus, researchers have dedicated significant effort to diverse implementations for them. Benefiting from recent progress in large language models…

Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently,…

计算与语言 · 计算机科学 2024-04-22 Taicheng Guo , Xiuying Chen , Yaqi Wang , Ruidi Chang , Shichao Pei , Nitesh V. Chawla , Olaf Wiest , Xiangliang Zhang

Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges…

With the recent emergence of revolutionary autonomous agentic systems, research community is witnessing a significant shift from traditional static, passive, and domain-specific AI agents toward more dynamic, proactive, and generalizable…

计算机视觉与模式识别 · 计算机科学 2025-10-14 Huanjin Yao , Ruifei Zhang , Jiaxing Huang , Jingyi Zhang , Yibo Wang , Bo Fang , Ruolin Zhu , Yongcheng Jing , Shunyu Liu , Guanbin Li , Dacheng Tao

Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential…

人工智能 · 计算机科学 2024-04-10 Saikat Barua

The pursuit of human-level artificial intelligence (AI) has significantly advanced the development of autonomous agents and Large Language Models (LLMs). LLMs are now widely utilized as decision-making agents for their ability to interpret…

The era of intelligent agents is upon us, driven by revolutionary advancements in large language models. Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical…

Due to the powerful capabilities demonstrated by large language model (LLM), there has been a recent surge in efforts to integrate them with AI agents to enhance their performance. In this paper, we have explored the core differences and…

计算与语言 · 计算机科学 2023-09-27 Pengyu Zhao , Zijian Jin , Ning Cheng

The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language interaction.…

人工智能 · 计算机科学 2026-05-05 Guannan Liang , Qianqian Tong

Large Language Model (LLM)-based agents have emerged as a new paradigm that extends LLMs' capabilities beyond text generation to dynamic interaction with external environments. By integrating reasoning with perception, memory, and tool use,…

人工智能 · 计算机科学 2025-09-23 Minxing Zhang , Yi Yang , Roy Xie , Bhuwan Dhingra , Shuyan Zhou , Jian Pei

The rise of large language models (LLMs) has sparked a surge of interest in agents, leading to the rapid growth of agent frameworks. Agent frameworks are software toolkits and libraries that provide standardized components, abstractions,…

软件工程 · 计算机科学 2025-12-02 Yanlin Wang , Xinyi Xu , Jiachi Chen , Tingting Bi , Wenchao Gu , Zibin Zheng

Large language models (LLMs) have achieved superior performance in powering text-based AI agents, endowing them with decision-making and reasoning abilities akin to humans. Concurrently, there is an emerging research trend focused on…

计算机视觉与模式识别 · 计算机科学 2024-02-26 Junlin Xie , Zhihong Chen , Ruifei Zhang , Xiang Wan , Guanbin Li

AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have…

人工智能 · 计算机科学 2025-07-03 Yinwang Ren , Yangyang Liu , Tang Ji , Xun Xu

While contemporary large language models (LLMs) are increasingly capable in isolation, there are still many difficult problems that lie beyond the abilities of a single LLM. For such tasks, there is still uncertainty about how best to take…

人工智能 · 计算机科学 2026-02-27 Ryan Liu , Dilip Arumugam , Cedegao E. Zhang , Sean Escola , Xaq Pitkow , Thomas L. Griffiths

The development of AI agents based on large, open-domain language models (LLMs) has paved the way for the development of general-purpose AI assistants that can support human in tasks such as writing, coding, graphic design, and scientific…

人工智能 · 计算机科学 2025-06-03 Mustafa Mert Çelikok , Saptarashmi Bandyopadhyay , Robert Loftin

Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity. As such, game agents offer a valuable testbed for exploring capabilities relevant to Artificial General Intelligence. Recently, the…

The arrival of Large Language Models (LLMs) has stirred up philosophical debates about the possibility of realizing agency in an artificial manner. In this work we contribute to the debate by presenting a theoretical model that can be used…

人工智能 · 计算机科学 2024-07-25 Maud van Lier , Gorka Muñoz-Gil

The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs…

软件工程 · 计算机科学 2025-12-04 Junwei Liu , Kaixin Wang , Yixuan Chen , Xin Peng , Zhenpeng Chen , Lingming Zhang , Yiling Lou

The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across…

The concept of the 'agent' has profoundly shaped Artificial Intelligence (AI) research, guiding development from foundational theories to contemporary applications like Large Language Model (LLM)-based systems. This paper critically…

人工智能 · 计算机科学 2025-09-16 Jesse Gardner , Vladimir A. Baulin
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