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

**Authors:** 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  
**arXiv:** [2504.01990](https://arxiv.org/abs/2504.01990)  
**Date:** 2025-03-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2504.01990v1/

## Key points

- The advent of large language models (LLMs) has revolutionized artificial intelligence, enabling the development of advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains.
- Intelligent agents within a modular, brain-inspired architecture integrate principles from cognitive science, neuroscience, and computational research.
- Modular Foundation: Mapping cognitive, perceptual, and operational modules onto human brain functionalities.
- Self-Enhancement and Adaptive Evolution: Agents refine capabilities through automated optimization paradigms like AutoML and LLM-driven strategies.
- Collaborative and Evolutionary Multi-Agent Systems: Investigating collective intelligence emerging from agent interactions and societal structures.
- Building Safe AI Systems: Addressing the imperative of constructing safe, secure, and beneficial AI systems with security threats mitigation strategies.

## Summary

The advent of large language models (LLMs) has revolutionized artificial intelligence, enabling the development of advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. These agents are at the forefront of AI research and practical applications, presenting complex challenges in their design, evaluation, and continuous improvement. This comprehensive survey explores intelligent agents within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research. The exploration is structured into four interconnected parts
1. Modular Foundation: The survey systematically maps cognitive, perceptual, and operational modules of intelligent agents onto analogous human brain functionalities. Core components such as memory, world modeling, reward processing, and emotion-like systems are elucidated. 2. Self-Enhancement and Adaptive Evolution: The survey delves into how agents autonomously refine their capabilities, adapt to dynamic environments, and achieve continual learning through automated optimization paradigms like AutoML and LLM-driven strategies. 3. Collaborative and Evolutionary Multi-Agent Systems: The collective intelligence emerging from agent interactions, cooperation, and societal structures is investigated. Parallels to human social dynamics are highlighted. 4. Building Safe AI Systems: The critical imperative of constructing safe, secure,and beneficial AI systems is addressed. Intrinsic and extrinsic security threats,
ethical alignment,
robustness considerations,
as well as practical mitigation strategies necessary for trustworthy real-world deployment are emphasized. Additionally,
recent research on the planning abilities of large language models is discussed in various studies cited in the context provided above. These studies delve into understanding how LLMs can reason
and plan effectively in different scenarios using innovative approaches such as automated planning frameworks
and memory-enhanced reasoning techniques. Overall,
this refined summary underscores the transformative impact of large language models on AI development while highlighting the ongoing efforts to enhance the capabilities of intelligent agents through interdisciplinary research and innovative methodologies.