Abstract
Intelligent systems are widely assumed to improve through learning, coordination, and optimization. However, across domains -- from artificial intelligence to economic institutions and biological evolution -- increasing intelligence often precipitates paradoxical degradation: systems become rigid, lose adaptability, and fail unexpectedly. We identify \emph{entropy collapse} as a universal dynamical failure mode arising when feedback amplification outpaces bounded novelty regeneration. Under minimal domain-agnostic assumptions, we show that intelligent systems undergo a sharp transition from high-entropy adaptive regimes to low-entropy collapsed regimes. Collapse is formalized as convergence toward a stable low-entropy manifold, not a zero-entropy state, implying a contraction of effective adaptive dimensionality rather than loss of activity or scale. We analytically establish critical thresholds, dynamical irreversibility, and attractor structure and demonstrate universality across update mechanisms through minimal simulations. This framework unifies diverse phenomena -- model collapse in AI, institutional sclerosis in economics, and genetic bottlenecks in evolution -- as manifestations of the same underlying process. By reframing collapse as a structural cost of intelligence, our results clarify why late-stage interventions systematically fail and motivate entropy-aware design principles for sustaining long-term adaptability in intelligent systems. \noindent\textbf{Keywords:} entropy collapse; intelligent systems; feedback amplification; phase transitions; effective dimensionality; complex systems; model collapse; institutional sclerosis
Abstract (translated)
智能系统被广泛认为是通过学习、协调和优化来改进的。然而，在从人工智能到经济制度以及生物进化等多个领域中，随着系统的智能化程度提高，反而常常导致令人困惑的质量下降：系统变得僵化，失去适应性，并且意外失败。 我们确定“熵崩溃”为一种普遍的动力学失效模式，这种现象发生在反馈放大超过有限新颖性的再生时。在最少的无域特定假设下，我们展示智能系统经历了从高熵适应性阶段到低熵崩溃阶段的急剧转变。这里的崩溃被定义为收敛于一个稳定的低熵流形，而不是零熵状态，这意味着有效适应维度减少，而非活动或规模的丧失。 我们通过最小化模拟验证了关键阈值、动力学不可逆性和吸引子结构，并证明这种框架在更新机制中具有普适性。该理论框架统一了解释AI中的模型崩溃现象、经济学中的制度硬化问题以及进化过程中遗传瓶颈等不同领域中的多种现象，这些现象本质上都是同一个底层过程的表现。 通过将熵崩溃重新定义为智能的一种结构性成本，我们的研究结果阐明了为何后期干预通常无效，并激励人们采用考虑熵的设计原则来维持智能系统的长期适应性。关键词：熵崩溃；智能系统；反馈放大；相变；有效维度；复杂系统；模型坍塌；制度硬化
URL
https://arxiv.org/abs/2512.12381
