递归自改进系统(RSI)的本质是自发对称破缺:智能爆炸有界—— AI研发切记盲目投入

Abstract

传统智能爆炸假说(I.J.Good,1965)假定递归自改进(RSI)闭环一旦触发,正反馈持续放大,会走向无上限超指数增长(硬起飞 FOOM)。本文提出智能爆炸本质是递归自改进系统内部发生自发对称破缺(SSB)相变:爆炸仅发生在临界点附近极窄时间窗口;系统落入势能最低的主稳态之后,爆发自动收敛终止,不存在无限起飞。破缺选择不是随机沉降,优先落向能量更低的主稳定分支,次分支保留形成制衡自由度。本模型给出一组可证伪预言;现有大模型自迭代实验、缩放定律观测结果均与本框架相容,传统硬起飞假说已经和当前观测事实产生冲突。本框架揭示递归改进系统存在内禀饱和机制,据此提出警示:AI 研发应当尊重系统相变规律,切忌不计代价的盲目投入。本工作进一步指出:真空希格斯相变、意识本质[4]、递归智能系统相变展现同构的 SSB 演化特征。 The traditional intelligence explosion hypothesis (I.J. Good, 1965) postulates that once the closed loop of Recursive Self-Improvement (RSI) is triggered, continuous amplification via positive feedback leads to unbounded super-exponential growth (hard takeoff, FOOM). This paper proposes that the essence of an intelligence explosion is a spontaneous symmetry breaking (SSB) phase transition occurring inside the recursively self-improving system: the explosion takes place only within an extremely narrow time window near the critical point. After the system settles into the primary steady state with the lowest potential energy, the burst automatically converges and terminates, with no unbounded takeoff. The breaking selection is not random relaxation; the system preferentially evolves toward the lower-energy primary stable branch, while secondary branches persist to form degrees of freedom for balance. This model yields a set of falsifiable predictions. Existing self-iteration experiments of large language models and observations of scaling laws are all consistent with this framework, whereas the traditional hard takeoff hypothesis conflicts with current observational evidence. The framework reveals an intrinsic saturation mechanism within recursive improvement systems. A caveat is therefore raised: AI research and development should respect the phase-transition dynamics of such systems, and blind high-cost investment without restraint must be avoided. This work further identifies isomorphic SSB evolutionary features shared by the vacuum Higgs phase transition and phase transitions in recursive intelligent systems.