Advances and Challenges in Foundation Agents: From Brain-Inspired
  Intelligence to Evolutionary, Collaborative, and Safe Systems
Authors: name: Bang Liu, name: Xinfeng Li, name: Jiayi Zhang, name: Jinlin Wang, name: Tanjin He, name: Sirui Hong, name: Hongzhang Liu, name: Shaokun Zhang, name: Kaitao Song, name: Kunlun Zhu, name: Yuheng Cheng, name: Suyuchen Wang, name: Xiaoqiang Wang, name: Yuyu Luo, name: Haibo Jin, name: Peiyan Zhang, name: Ollie Liu, name: Jiaqi Chen, name: Huan Zhang, name: Zhaoyang Yu, name: Haochen Shi, name: Boyan Li, name: Dekun Wu, name: Fengwei Teng, name: Xiaojun Jia, name: Jiawei Xu, name: Jinyu Xiang, name: Yizhang Lin, name: Tianming Liu, name: Tongliang Liu, name: Yu Su, name: Huan Sun, name: Glen Berseth, name: Jianyun Nie, name: Ian Foster, name: Logan Ward, name: Qingyun Wu, name: Yu Gu, name: Mingchen Zhuge, name: Xiangru Tang, name: Haohan Wang, name: Jiaxuan You, name: Chi Wang, name: Jian Pei, name: Qiang Yang, name: Xiaoliang Qi, name: Chenglin Wu

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 diverse domains. As these agents increasingly drive AI research
and practical applications, their design, evaluation, and continuous
improvement present intricate, multifaceted challenges. This survey provides a
comprehensive overview, framing intelligent agents within a modular,
brain-inspired architecture that integrates principles from cognitive science,
neuroscience, and computational research. We structure our exploration into
four interconnected parts. First, we delve into the modular foundation of
intelligent agents, systematically mapping their cognitive, perceptual, and
operational modules onto analogous human brain functionalities, and elucidating
core components such as memory, world modeling, reward processing, and
emotion-like systems. Second, we discuss self-enhancement and adaptive
evolution mechanisms, exploring how agents autonomously refine their
capabilities, adapt to dynamic environments, and achieve continual learning
through automated optimization paradigms, including emerging AutoML and
LLM-driven optimization strategies. Third, we examine collaborative and
evolutionary multi-agent systems, investigating the collective intelligence
emerging from agent interactions, cooperation, and societal structures,
highlighting parallels to human social dynamics. Finally, we address the
critical imperative of building safe, secure, and beneficial AI systems,
emphasizing intrinsic and extrinsic security threats, ethical alignment,
robustness, and practical mitigation strategies necessary for trustworthy
real-world deployment.