Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems (2504.01990v1)

Abstract: The advent of 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.

Okay, here is a detailed summary of the survey paper "Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems" (Liu et al., 31 Mar 2025 ).

Overall Goal and Motivation:

The paper provides a comprehensive survey of the rapidly advancing field of intelligent agents, particularly those powered by LLMs. It argues that while LLMs provide powerful foundational capabilities (the "engine"), they are not yet fully intelligent agents (the "vehicle"). The survey aims to bridge this gap by proposing a brain-inspired modular framework for designing, understanding, and evaluating "Foundation Agents." It seeks to synthesize insights from cognitive science, neuroscience, AI research, and practical applications to identify key challenges, research gaps, and future opportunities, ultimately guiding the development of capable, adaptive, collaborative, and safe AI systems beneficial to society.

Structure and Framework:

The survey is structured into four main parts, built upon a proposed modular, brain-inspired agent framework.

1. Introduction and Foundational Concepts:
   • Context: Traces the history of AI agents from early concepts to the transformative impact of LLMs.
   • Brain Analogy: Compares human cognition (biological hardware, consciousness, learning, creativity) with LLM agents (Table 1), highlighting key differences and potential inspirations. It maps major human brain regions (frontal, parietal, temporal, occipital lobes, cerebellum, brainstem, limbic system) to AI functions, classifying AI progress (L1-L3) in these areas (Figure 1).
   • Proposed Framework: Introduces a general agent framework based on a Perception-Cognition-Action loop (Figure 2), enriched with components like memory, world model, emotion, goals, and reward systems (Table 2/3). It distinguishes between learning ($\mathrm{L}$) and reasoning ($\mathrm{R}$) within cognition ($\mathrm{C}$).
   • Foundation Agent Definition: Formally defines a "Foundation Agent" as an autonomous, adaptive system with capabilities for active perception, dynamic cognitive adaptation (updating memory, world models, goals, emotions, rewards), autonomous reasoning/planning, purposeful action (internal/external), and collaborative multi-agent structuring.
   • Biological Inspirations & Theories: Discusses how components like memory (hippocampus/neocortex), world models (predictive processing), emotion (limbic system), goals/reward (PFC/subcortical), and reasoning (PFC) draw inspiration from neuroscience. Connects the framework to existing theories like Minsky's Society of Mind, Buzsáki's Inside-Out perspective, POMDPs, and Active Inference.
2. Part I: Core Components of Intelligent Agents: This part explores the modules of the proposed framework.
   • Cognition: The "brain" of the agent.
     • Learning: Explores how agents learn, distinguishing between Full Mental State Learning (modifying core model parameters via SFT, PEFT, RLHF, DPO, RL) and Partial Mental State Learning (updating specific components like memory or world models via ICL, interaction, reflection). Discusses learning Objectives: improving Perception (multimodal fusion, retrieval), Reasoning (data distillation, RL, bootstrapping), and World Understanding (experiential learning, reflection, reward refinement, using LLMs as world models).
     • Reasoning: Formalized as selecting action $a_t$ based on mental state $M_t$. Contrasts Structured Reasoning (explicit steps like linear, tree, graph structures; static structures like ensembles, progressive improvement, error correction; domain-specific frameworks) with Unstructured Reasoning (holistic/implicit steps via prompting, specialized reasoning models, implicit latent-space reasoning). Discusses Planning as a specialized reasoning form involving task decomposition, search, and world knowledge integration.
   • Memory: Compares human memory (Sensory, STM/Working, LTM - Declarative/Implicit) with AI agent memory representations (Sensory, Short-term/Context/Working, Long-term/Semantic/Episodic/Procedural). Details the Memory Lifecycle: Acquisition (compression, consolidation), Encoding (attention, fusion), Derivation (reflection, summarization, distillation, forgetting), Retrieval/Matching, Neural Memory Networks (associative, parameter integration), and Utilization (RAG, long-context, hallucination mitigation).
   • World Model: Compares human mental models (predictive, integrative, adaptive, multi-scale) with AI world models. Categorizes AI paradigms: Implicit (latent state models, LLMs as simulators), Explicit (factorized transition/observation models, model-based RL), Simulator-Based (external engines, real-world interaction), and Hybrid/Instruction-Driven. Discusses relationships with Memory, Perception, and Action modules.
   • Reward: Contrasts human neurochemical reward pathways with AI reward functions. Categorizes AI paradigms: Extrinsic (Dense, Sparse, Delayed, Adaptive), Intrinsic (Curiosity, Diversity, Competence, Exploration, Info Gain), Hybrid, and Hierarchical. Discusses interactions with other modules and challenges (sparsity, hacking, shaping, multi-objective, misspecification).
   • Emotion Modeling: Discusses psychological foundations (Categorical, Dimensional, Hybrid/Componential, Neurocognitive theories) and their relevance to AI. Explores how emotions are incorporated into agents, how agents understand human emotions (textual/multimodal analysis), how AI emotions/personality are analyzed/modeled, and methods for manipulating AI emotional responses. Highlights ethical concerns (manipulation, privacy, alignment, mimicry vs. real experience).
   • Perception: Compares human vs. AI senses (range, efficiency, integration). Details AI perception representations: Unimodal (Text, Image, Video, Audio), Cross-modal (Text-Image, Text-Video, etc.), Multimodal (VLM, VLA, ALM, AVLM). Discusses optimizing perception systems (Model, System, External feedback) and applications.
   • Action Systems: Contrasts human action (Mental vs. Physical) with agentic action (distinct from base models like LLMs). Details AI action system paradigms: Action Space (Language: Text/Code/Chat; Digital: Game/Multimodal/Web/GUI/DB/KG; Physical: Robotics), Action Learning (ICL: Prompt/Decompose/Role-play/Refine; PT/SFT: Pre-Train/SFT; RL: LLM-guided exploration, Hierarchical RL), and Tool-Based Action (Tool Types: Language/Digital/Physical/Scientific; Tool Learning: Discovery/Creation/Usage). Discusses the Action vs. Perception "Inside-Out" perspective.
3. Part II: Self-Evolution in Intelligent Agents: Focuses on agents autonomously improving themselves.
   • Motivation: Need for automation in agent design/improvement (like AutoML), benefits of scalability and cost reduction.
   • Optimization Spaces: Identifies key areas for optimization: Prompts, Agentic Workflows (Edge representations: Graph/NN/Code; Node parameters: Format/Temperature/Prompt/Model), and Tools (Learning to use, Creating new tools, Evaluation).
   • LLMs as Optimizers: Explores using LLMs for optimization. Compares with traditional methods (Gradient-based, Zeroth-order). Discusses iterative LLM optimization approaches (Random Search, Gradient Approximations, Bayesian Optimization/Surrogate Modeling). Covers optimization hyperparameters, optimization across depth/time, and theoretical perspectives (In-Context Learning, Mechanistic Interpretability, limitations under uncertainty).
   • Online vs. Offline Improvement: Contrasts Online self-improvement (real-time feedback, reflection, active exploration, reward shaping, dynamic tuning) with Offline self-improvement (batch updates, fine-tuning, meta-optimization, reward calibration). Discusses Hybrid approaches integrating both.
   • Scientific Discovery: Frames scientific discovery as a form of self-evolution. Defines intelligence via KL divergence. Discusses agent-knowledge interactions: Hypothesis Generation/Testing, Protocol Planning/Tool Innovation, Data Analysis/Implication Derivation. Highlights technological readiness challenges (real-world interaction, complex reasoning, prior knowledge integration).
4. Part III: Collaborative and Evolutionary Intelligent Systems: Examines multi-agent systems (MAS).
   • System Design: Categorizes MAS based on goals/norms: Strategic Learning (game theory, competition/cooperation), Modeling/Simulation (independent agents, emergent phenomena), Collaborative Task Solving (shared goals, structured workflows).
   • Composition & Protocol: Discusses agent types (Homogeneous vs. Heterogeneous - by personas, observation space, action space) and the possibility of evolution from homogeneity. Covers Interaction Protocols: Message Types (Structured vs. Unstructured), Communication Interfaces (Agent-Environment, Agent-Agent, Human-Agent), and Next-Gen Protocols (IoA, MCP, ANP, Agora) comparing centralization, flexibility, etc.
   • Topology: Analyzes communication structures: Static Topologies (Layered/Hierarchical, Decentralized, Centralized) vs. Dynamic/Adaptive Topologies (Search-based, LLM-based generation, External parameters). Discusses scalability challenges and solutions (communication overhead, bottlenecks, optimal agent count vs. simulation needs, hybrid architectures, specialized platforms like AgentScope, Project Sid, AgentSociety).
   • Collaboration Paradigms: Details Agent-Agent Collaboration types (Consensus-oriented, Collaborative Learning, Teaching/Mentoring, Task-oriented). Discusses Human-AI Collaboration modes (Delegation, Interactive instruction, Immersive). Examines Collaborative Decision-Making mechanisms (Dictatorial vs. Collective - Voting/Debate).
   • Evolution & Adaptation: Explores Collective Intelligence (Improved performance, Emergent behaviors like deception/trust, Social evolution like norm formation/role specialization). Discusses Individual Adaptability mechanisms (Memory-based learning, Shared memory-based learning, Parameter-based learning like co-fine-tuning).
   • Evaluation: Reviews benchmarks for specific reasoning tasks (Code, Knowledge, Math, Societal Simulation) and general MAS capabilities (Collaboration-focused, Competition-focused, Adaptive/Resilience benchmarks). Notes challenges in standardization, scalability, and diversity evaluation.
5. Part IV: Building Safe and Beneficial AI Agents: Addresses safety, security, and alignment.
   • Framework: Introduces a framework distinguishing Intrinsic Safety threats (within agent components: Brain, Perception, Action) and Extrinsic Safety threats (from interactions: Agent-Memory, Agent-Agent, Agent-Environment).
   • Intrinsic Threats (Brain - LLM):
     • Safety: Jailbreaks (White-box/Black-box), Prompt Injection (Direct/Indirect), Hallucination (Knowledge-conflict/Context-conflict), Misalignment (Goal-misguided/Capability-misused), Poisoning Attacks (Model/Data/Backdoor). Includes formalizations and mitigation strategies (often training-free).
     • Privacy: Training Data Inference (Membership Inference, Data Extraction), Interaction Data Inference (System Prompt Stealing, User Prompt Stealing). Discusses mitigations (DP, FL, HE, TEE, MPC, Watermarking, Unlearning).
   • Intrinsic Threats (Non-Brain):
     • Perception: Adversarial Attacks (Textual, Visual, Auditory, Other modalities), Misperception Issues (dataset bias, environmental complexity, model limits). Discusses mitigations.
     • Action: Supply Chain Attacks (compromising external services/tools), Risks in Tool Usage (unauthorized actions, data leakage, excessive permissions). Discusses mitigations.
   • Extrinsic Threats: Agent-Memory Interaction (RAG attacks), Agent-Environment Interaction (Physical: sensor spoofing, actuator manipulation, hazard exploitation; Digital: code injection, data manipulation, DoS, resource exhaustion), Agent-Agent Interaction (Competitive: misinformation, exploitation, DoS, collusion; Cooperative: leakage, error propagation, compromise, sync issues). Discusses domain-specific safety protocols (general-purpose vs. specialized).
   • Superalignment & Scaling Law: Introduces Superalignment as goal-driven alignment beyond RLHF, using composite objectives (Task, Goal, Norm). Discusses Safety Scaling Law: the non-linear relationship between capability and risk, trade-offs (Helpfulness-Safety), Commercial vs. Open-Source dynamics, scale-data interplay, multimodal vulnerabilities. Covers strategies like preference alignment, controllable design (AI-45 Rule), and risk management frameworks (Red/Yellow Lines).
6. Concluding Remarks and Future Outlook:
   • Summary: Recaps the survey's contributions across agent components, self-evolution, collaboration, and safety.
   • Future Vision: Predicts key milestones: general-purpose agents, continuous environmental learning/self-evolution, collective intelligence network effects transforming human know-how sharing, and new paradigms for large-scale human-AI collaboration driving societal transformation.

This detailed summary covers the main sections, key concepts, definitions, classifications, examples, challenges, and future directions presented in the survey paper, reflecting its comprehensive and interdisciplinary approach to foundation agents.