基礎代理的進展與挑戰：從類腦智能到演化、協作與安全系統

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

March 31, 2025

作者: Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tanjin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, Yuheng Cheng, Suyuchen Wang, Xiaoqiang Wang, Yuyu Luo, Haibo Jin, Peiyan Zhang, Ollie Liu, Jiaqi Chen, Huan Zhang, Zhaoyang Yu, Haochen Shi, Boyan Li, Dekun Wu, Fengwei Teng, Xiaojun 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, Xiangru Tang, Haohan Wang, Jiaxuan You, Chi Wang, Jian Pei, Qiang Yang, Xiaoliang Qi, Chenglin Wu

cs.AI

摘要

大型語言模型（LLMs）的出現催化了人工智慧領域的轉型變革，為能夠進行複雜推理、具備穩健感知能力並在多樣化領域中靈活行動的高級智能代理鋪平了道路。隨著這些代理日益驅動著AI研究與實際應用，其設計、評估及持續改進面臨著錯綜複雜的多方面挑戰。本綜述提供了一個全面的概覽，將智能代理置於一個模組化、受大腦啟發的架構中，該架構整合了認知科學、神經科學及計算機研究的原理。我們將探索分為四個相互關聯的部分。首先，深入探討智能代理的模組化基礎，系統地將其認知、感知及操作模組對應於人類大腦的類似功能，並闡明諸如記憶、世界建模、獎勵處理及類情感系統等核心組件。其次，討論自我增強與適應性進化機制，探討代理如何自主精進其能力、適應動態環境，並通過自動化優化範式（包括新興的AutoML及LLM驅動的優化策略）實現持續學習。第三，考察協作與進化的多代理系統，研究代理間互動、合作及社會結構中湧現的集體智慧，強調其與人類社會動態的相似之處。最後，探討構建安全、可靠且有益的AI系統的關鍵必要性，強調內在與外在的安全威脅、倫理對齊、魯棒性以及實現可信賴實際部署所需的實用緩解策略。

English

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.