为大模型智能体设计经济模型，平衡输出质量与成本消耗。
Token Economics for LLM Agents: A Dual-View Study from Computing and Economics
- 从计算与经济双视角建模令牌作为生产要素、交易媒介和计价单位。
- 提出四维分类框架：单智能体优化、多智能体协作、生态拥堵治理与安全威胁内化。
- 适合研究智能体系统架构、经济机制设计及可持续部署的开发者与学者。
随着大模型智能体的发展，令牌已成为智能体人工智能的核心经济单元。然而，其指数级消耗带来了严重的计算、协作与安全瓶颈。现有综述分散于系统优化、架构设计与信任问题，缺乏统一框架来评估输出质量与经济成本之间的根本权衡。为此，本文首次提出全面的令牌经济学综述。通过融合计算机科学与经济学，我们将令牌概念化为生产要素、交换媒介与计价单位，并构建一个四维分类体系：(1) 微观层（单智能体）：基于新古典企业理论，优化预算约束下的因子替代；(2) 中观层（多智能体系统）：运用交易成本与委托-代理理论，降低协作摩擦；(3) 宏观层（智能体生态系统）：通过机制设计应对拥堵外部性并实现合理定价；(4) 安全性：将对抗性威胁内化为内生经济约束。最后，我们展望前沿方向，包括可微分令牌预算与动态市场，为下一代可扩展智能体系统奠定理论基础。
原文摘要 · Abstract (English)
As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe computational, collaborative, and security bottlenecks. Current surveys remain fragmented across system optimization, architecture design, and trust, lacking a unified framework to evaluate the fundamental trade-off between output quality and economic cost. To bridge this gap, this survey presents the first comprehensive survey of Token Economics. By unifying computer science and economics, we conceptualize tokens as production factors, exchange mediums, and units of account. We synthesize existing literature across a four-dimensional taxonomy: (1) Micro-level (Single Agent): Optimizing budget-constrained factor substitution via neoclassical firm theory. (2) Meso-level (Multi-Agent Systems): Minimizing collaboration friction using transaction cost and principal-agent theories. (3) Macro-level (Agent Ecosystems): Addressing congestion externalities and pricing via mechanism design. (4) Security: Internalizing adversarial threats as endogenous economic constraints. Finally, we outline frontier directions, including differentiable token budgets and dynamic markets, to lay the theoretical foundation for scalable next-generation agent systems.
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