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

2025-03-31

AI Summary

This paper addresses systemic challenges in large language model (LLM)-driven intelligent agents—specifically, complex reasoning, cross-domain adaptation, safety and trustworthiness, and multi-agent collaboration. To this end, it proposes the first brain-inspired four-dimensional unified framework: (1) modular foundational architecture, (2) self-enhancing evolutionary mechanism, (3) collaborative emergence paradigm, and (4) endogenous security design. Methodologically, the framework integrates cognitive modeling, world models, LLM-driven AutoML optimization, multi-agent game-theoretic alignment, robustness verification, and embedded ethical constraints. Its primary contribution is a holistic “theory–method–evaluation” framework spanning the agent’s entire lifecycle, enabling organic integration and autonomous evolution of perception–cognition–action modules. Empirical results demonstrate significant improvements in agent safety, adaptive capability, and socio-collaborative competence. The framework establishes a novel paradigm for next-generation trustworthy, adaptive, and embodied AI systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Designing brain-inspired modular agents for cognitive and perceptual tasks
• Enabling autonomous self-improvement and adaptation in dynamic environments
• Ensuring safety and ethical alignment in collaborative multi-agent systems

Innovation

• Modular brain-inspired architecture integrates cognitive principles
• Self-enhancement via AutoML and LLM-driven optimization
• Collaborative multi-agent systems mimic human social dynamics