title: Advances and Challenges in Foundation Agents: From Brain-Inspired
    Intelligence to Evolutionary, Collaborative, and Safe Systems
summary: 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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paper_title: Advances and Challenges in Foundation Agents: From Brain-Inspired
    Intelligence to Evolutionary, Collaborative, and Safe Systems
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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