This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Created by
Haebom
Author
Bang Liu, Jia, Jiawei Xu, Jinyu Xiang, Yizhang Lin, Tianming Liu, Tongliang Liu, Yu Su, Huan Sun, Glen Berseth, Jianyun Nie, Ian Foster, Logan Ward, Qingyun Wu, Yu Gu, Mingchen Zhuge, Xinbing Liang, Xiangru Tang, Haohan Wang, Jiaxuan You, Chi Wang, Jian Pei, Qiang Yang, Xiaoliang Qi, Chenglin Wu
Outline
This paper provides a comprehensive overview of intelligent agents, which are rapidly evolving due to advances in large-scale language models (LLMs). By structuring intelligent agents within a modular, brain-based architecture that integrates principles from cognitive science, neuroscience, and computational research, we systematically map cognitive, perceptual, and operational modules—including core components such as memory, world modeling, reward processing, goals, and emotions—to human brain functions. Furthermore, we address self-improvement and adaptive evolution mechanisms for agents, multi-agent systems, and strategies for building safe and beneficial AI systems. By integrating insights from various fields into a modular AI architecture, we address key research challenges and opportunities, fostering innovation that aligns technological advancement with societal benefits.
Takeaways, Limitations
•
Takeaways:
◦
A modular brain-based architecture offers a new perspective on intelligent agent design and evaluation.
◦
Exploring ways to continuously learn and adapt to the environment through self-improvement and adaptive evolution mechanisms.
◦
Research on collective intelligence and cooperation in multi-agent systems, and analysis of social structures
◦
Presenting ethical and technical strategies for building safe and beneficial AI systems.
◦
Presenting a new direction for AI research through the convergence of cognitive science, neuroscience, and computing research.
•
Limitations:
◦
Due to the lack of specific algorithms or experimental results, verification of practical feasibility is required.
◦
Lack of detailed description of interactions and integration between modules
◦
Further research is needed to determine the effectiveness of strategies for building safe and beneficial AI systems.
◦
The difficulty of multidisciplinary research that requires collaboration between experts from various fields
