AIvilization v0: Toward Large-Scale Artificial Social Simulation with a Unified Agent Architecture and Adaptive Agent Profiles

The evolution of large language models (LLMs) has enabled the development of autonomous agents capable of complex reasoning and interaction. Early systems like GPT-based agents demonstrated basic task execution, but lacked long-term goal stability. Generative agents (Gordon et al., 2022) introduced memory and reflection, improving coherence. Scaling to multi-agent societies, platforms like OpenAI's AutoGPT and BabyAGI enabled multi-step reasoning but remained limited in environment complexity and validation. Agent-based economic models (e.g., NetLogo, MASON) simulated social interactions but lacked integration with LLMs for nuanced behavior. Recent efforts focus on combining these approaches, yet challenges persist in ensuring long-term goal persistence, environment adaptability, and validation. Existing systems often suffer from brittle plans, limited scalability, or unrealistic social dynamics. AIvilization v0 advances this landscape by integrating hierarchical goal decomposition, simulation validation, and dual memory within a tightly coupled socio-economic environment, addressing these gaps and enabling large-scale, realistic social simulations.

Achieving long-term goal stability in dynamic, multi-objective social environments remains a core challenge. Traditional planning methods either produce rigid, brittle plans vulnerable to environmental shifts or rely on reactive behaviors that lack strategic coherence. The coupling of multiple subsystems—production, trade, social interaction—further complicates decision-making, as each action influences subsequent constraints and opportunities. Ensuring actions remain feasible amid resource fluctuations, institutional changes, and social dynamics is critical. Existing approaches lack integrated validation mechanisms to verify plan executability before execution, leading to failures and reduced realism. Addressing these issues requires a unified architecture capable of decomposing, validating, and adapting long-term strategies in real-time, while maintaining a coherent social identity.

The key innovations include: 1) Hierarchical Branch-Thinking Planner, which decomposes long-term goals into parallel branches and sub-tasks, enabling multi-objective management; 2) Simulation-Guided Validation, predicting action outcomes to prevent infeasible plans; 3) Dual-process Memory, separating fast execution traces from slow semantic consolidation to sustain identity; 4) Human-in-the-loop Steering, integrating long-term objectives and short commands via memory channels. These components work synergistically to balance strategic coherence with environmental responsiveness, a significant departure from prior static or single-goal systems. The environment design, combining physiological costs, multi-tier production, AMM pricing, and gated occupations, creates a realistic, tightly coupled socio-economic ecosystem, facilitating emergent macro behaviors and individual portrait evolution.

• �� Decompose long-term goals into multiple parallel branches (personal, production, social). • Use a hierarchical planner to generate sub-tasks within each branch, prioritizing based on current state and environment. • Translate sub-tasks into atomic actions via a domain-specific micro-planner. • Before execution, simulate the action sequence using the Action Simulator to predict outcomes and detect infeasibilities like resource shortages or rule violations. • If issues are detected, apply lightweight heuristics (e.g., insert rest, reduce trade size) or escalate to full re-planning. • During execution, update short-term memory with outcomes, and consolidate experience into long-term memory to evolve identity. • Incorporate human commands at various abstraction levels through memory channels, influencing future planning. • The environment's physiological, economic, and social subsystems are tightly coupled, affecting action feasibility and outcomes.

The system was deployed with over 28,000 agents in a resource-constrained environment featuring multi-tier production, dynamic pricing, and social gating. Metrics included market price distributions, wealth stratification, individual portrait evolution, and response to human steering. Statistical measures such as excess kurtosis, autocorrelation, and Spearman correlation evaluated economic realism. Ablation studies tested the importance of goal decomposition and simulation validation by comparing variants with/without these components, assessing robustness and scalability. The experiments demonstrated stable market behaviors, realistic socioeconomic patterns, and coherent agent portraits over extended periods, validating the architecture’s effectiveness in complex, multi-objective scenarios.

The large-scale deployment revealed market behaviors consistent with stylized facts: heavy-tailed returns (median kurtosis 137.0), volatility clustering (autocorrelation 0.186), and education-driven wealth stratification (ρ=0.648). Individual portraits persisted coherently across hundreds of thousands of state transitions, with human steering significantly increasing short-term profile updates (average 14.51). Ablation experiments confirmed that goal decomposition and simulation validation are essential for handling multi-objective long-horizon tasks, with variants lacking these components showing degraded performance and stability. Overall, the system demonstrated high robustness, adaptability, and emergent macroeconomic phenomena, validating the architecture’s design principles.

This framework can be applied to economic policy testing, social behavior modeling, and autonomous agent development in virtual environments. Its ability to simulate realistic market dynamics and social stratification makes it valuable for policymakers and researchers. Additionally, the architecture supports building scalable, verifiable AI-driven societies for urban planning, resource management, and digital economy experiments, provided environment fidelity and computational costs are managed.

Despite promising results, environment realism remains limited by current simulation fidelity, hindering direct transfer to real-world scenarios. Computational costs are high, especially for simulation and re-planning in large populations, limiting real-time responsiveness. Social behaviors, while emergent, need further diversification to capture full human complexity. Future work should focus on environment realism, efficiency, and robustness to real-world data integration, ensuring broader applicability and safety.