提出可自组织的智能蜂群框架，实现动态人口调节与稳定控制。
Agentic Hives: Equilibrium, Indeterminacy, and Endogenous Cycles in Self-Organizing Multi-Agent Systems
- 构建可变规模的智能体群体，通过出生、死亡、分化实现自主演化。
- 证明均衡存在性与帕累托最优，并发现资源或目标变化时的重构规律。
- 揭示自组织系统中周期性波动与多重均衡现象，适合研究复杂系统治理者。
当前多智能体系统采用固定数量的智能体，其角色在设计时即确定。缺乏关于运行时创建、销毁或重新专业化智能体的正式理论，更未说明种群结构如何响应资源或目标的变化。本文提出「智能蜂群」框架：一群具备沙箱执行环境和语言模型访问权限的自主微智能体，经历出生、复制、专业化和死亡等人口动态过程。智能体家族充当生产部门，计算与内存作为生产要素，协调器兼具瓦尔拉斯拍卖师与全局工作空间功能。基于动态一般均衡的多部门增长理论（Benhabib & Nishimura, 1985；Venditti, 2005；Garnier, Nishimura & Venditti, 2013），我们证明了七项分析结果：(i) 借助布劳威尔不动点定理证明蜂群均衡的存在性；(ii) 均衡分配具有帕累托最优性；(iii) 在智能体家族间存在战略互补时，均衡多重性出现；(iv)-(v) 类似斯托珀-萨缪尔森与里布钦斯基定理，预测偏好与资源冲击下的蜂群重构机制；(vi) 通过霍普夫分支产生内生的人口周期；(vii) 给出局部渐近稳定性的充分条件。由此得到的参数空间分区图将区域划分为唯一均衡、不确定、内生周期与不稳定四类。结合比较静态矩阵，为操作者提供可预测并引导自组织多智能体系统演化的正式治理工具。
原文摘要 · Abstract (English)
Current multi-agent AI systems operate with a fixed number of agents whose roles are specified at design time. No formal theory governs when agents should be created, destroyed, or re-specialized at runtime-let alone how the population structure responds to changes in resources or objectives. We introduce the Agentic Hive, a framework in which a variable population of autonomous micro-agents-each equipped with a sandboxed execution environment and access to a language model-undergoes demographic dynamics: birth, duplication, specialization, and death. Agent families play the role of production sectors, compute and memory play the role of factors of production, and an orchestrator plays the dual role of Walrasian auctioneer and Global Workspace. Drawing on the multi-sector growth theory developed for dynamic general equilibrium (Benhabib \& Nishimura, 1985; Venditti, 2005; Garnier, Nishimura \& Venditti, 2013), we prove seven analytical results: (i) existence of a Hive Equilibrium via Brouwer's fixed-point theorem; (ii) Pareto optimality of the equilibrium allocation; (iii) multiplicity of equilibria under strategic complementarities between agent families; (iv)-(v) Stolper-Samuelson and Rybczynski analogs that predict how the Hive restructures in response to preference and resource shocks; (vi) Hopf bifurcation generating endogenous demographic cycles; and (vii) a sufficient condition for local asymptotic stability. The resulting regime diagram partitions the parameter space into regions of unique equilibrium, indeterminacy, endogenous cycles, and instability. Together with the comparative-statics matrices, it provides a formal governance toolkit that enables operators to predict and steer the demographic evolution of self-organizing multi-agent systems.
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