Advances and Challenges in Foundation Agents: From Brain-Inspired
  Intelligence to Evolutionary, Collaborative, and Safe Systems
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.
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Advances and Challenges in Foundation Agents: From Brain-Inspired
  Intelligence to Evolutionary, Collaborative, and Safe Systems
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.
This survey covers the design, evaluation, and improvement of intelligent agents based on modular, brain-inspired architectures, focusing on self-enhancement, multi-agent collaboration, and safety in AI systems.
large language models intelligent agents modular architecture brain-inspired cognitive science neuroscience computational research cognitive modules perceptual modules operational modules memory world modeling reward processing emotion-like systems self-enhancement adaptive evolution continual learning automated optimization AutoML LLM-driven optimization collaborative systems evolutionary systems collective intelligence social dynamics safe AI secure AI beneficial AI ethical alignment robustness