动态智能上限：测量人工智能系统长期规划和创造力的长期限制
Dynamic Intelligence Ceilings: Measuring Long-Horizon Limits of Planning and Creativity in Artificial Systems
AI总结：
本文提出动态智能上限概念，通过轨迹导向评估框架量化人工智能系统长期规划与创造力的限制，揭示智能上限的动态性与轨迹依赖性。
摘要：
近年来，人工智能的进步产生了在广泛任务上表现出色的系统。然而，这些进步越来越伴随着对长期发展行为的担忧，因为许多系统趋向于收敛到重复的解决方案模式，而不是持续增长。我们主张，当代人工智能系统的主要限制不在于能力本身，而在于其性能前沿的过早固定。为了解决这个问题，我们引入了动态智能上限（DIC）的概念，定义为在给定时间下，系统在当前资源、内部意图和结构配置下所能达到的最高有效智能水平。为了使这一概念具有实证可操作性，我们提出了一种以轨迹为中心的评估框架，将智能视为移动前沿而非静态快照。我们通过两个估计器来操作化DIC：渐进难度上限（PDC），捕捉在受限资源下可可靠解决的最大难度；以及上限漂移率（CDR），量化这一前沿的时间演变。这些估计器通过一个生成的基准测试进行实例化，该基准测试在单一受控环境中共同评估长期规划和结构创造力。我们的结果揭示了在固定解决方案流形内深化利用的系统与在时间内维持前沿扩展的系统的定性区别。重要的是，我们的框架不假设无限制的智能，而是将限制重新界定为动态的和轨迹依赖的，而不是静态的和过早固定的。
英文摘要：
Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many systems converge toward repetitive solution patterns rather than sustained growth. We argue that a central limitation of contemporary AI systems lies not in capability per se, but in the premature fixation of their performance frontier. To address this issue, we introduce the concept of a \emph{Dynamic Intelligence Ceiling} (DIC), defined as the highest level of effective intelligence attainable by a system at a given time under its current resources, internal intent, and structural configuration. To make this notion empirically tractable, we propose a trajectory-centric evaluation framework that measures intelligence as a moving frontier rather than a static snapshot. We operationalize DIC using two estimators: the \emph{Progressive Difficulty Ceiling} (PDC), which captures the maximal reliably solvable difficulty under constrained resources, and the \emph{Ceiling Drift Rate} (CDR), which quantifies the temporal evolution of this frontier. These estimators are instantiated through a procedurally generated benchmark that jointly evaluates long-horizon planning and structural creativity within a single controlled environment. Our results reveal a qualitative distinction between systems that deepen exploitation within a fixed solution manifold and those that sustain frontier expansion over time. Importantly, our framework does not posit unbounded intelligence, but reframes limits as dynamic and trajectory-dependent rather than static and prematurely fixed. \vspace{0.5em} \noindent\textbf{Keywords:} AI evaluation, planning and creativity, developmental intelligence, dynamic intelligence ceilings, complex adaptive systems
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
