提出动态智能天花板概念，衡量AI长期规划与创造力的演化极限
Dynamic Intelligence Ceilings: Measuring Long-Horizon Limits of Planning and Creativity in Artificial Systems
- 用轨迹评估框架将智能视为动态前沿而非静态指标
- 提出PDC与CDR两个指标，量化系统在资源约束下的最大可解难度和演化速率
- 在程序生成环境中验证：部分AI仅重复旧解，部分能持续拓展能力边界
近年来人工智能在多种任务中表现卓越，但其长期发展行为引发担忧——许多系统趋于重复性解法而非持续进化。本文认为当前AI的核心局限不在于能力本身，而在于性能边界过早固化。为此提出「动态智能天花板」（DIC）概念，指在特定资源、内在意图与结构配置下，系统在某一时刻所能达到的最高有效智能水平。为使该概念可实证，设计以轨迹为中心的评估框架，将智能视为移动的前沿而非静态快照。通过两个估算器实现：‘渐进难度天花板’（PDC）用于衡量受限资源下可稳定解决的最大难度，‘天花板漂移率’（CDR）则量化此前沿的时间演化。两者基于程序生成基准进行实例化，统一评估长时程规划与结构性创造力。结果揭示两类系统本质差异：一类深陷固定解空间的利用，另一类能持续扩展能力边界。本框架不假设无限智能，而是将限制重构为动态且依赖路径的特性。
原文摘要 · Abstract (English)
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
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