一篇理论论文，用热力学把智能测量变成可量化的事——递归自模拟不仅是特征，还是必要条件。适合想从原理上理解智能本质的人。
论文提出智能可定义为对罕见但有效未来的合法放大，即系统增加在被动动力学下本可能发生但受领域约束允许的结果的概率。其核心是递归自模拟架构：系统必须同时模拟世界及自身在其中的位置。主要结论给出了必要性陈述和条件近似充分性：高罕见有效提升（rare-valid lift）在热力学上必须依赖高保真内部模拟；当保真度高且模拟包含有效策略时，可达提升接近执行受限最优。该框架将智能置于从被动物质、反馈控制器、大型语言模型到人类文本生成器的统一可测量标度上。
Thermodynamic Measure of Intelligence
Can intelligence be measured? We propose that intelligence can be defined as the lawful amplification of rare but valid futures: a system increases the probability of outcomes that would be unlikely under passive dynamics but remain admissible under the constraints of the domain. We start with the premise that an intelligent system must model the world and its own place within it. Because the system is part of the world it models, this leads naturally to recursive self-simulation: the system represents futures in which its own actions are part of the trajectory. Our central results give a necessity statement and a conditional near-sufficiency statement connecting this architecture to a precise thermodynamic measure of lawful amplification of rare-valid futures: high rare-valid lift is impossible unless the internal simulation identifies rare-valid futures with high fidelity; conversely, when rare-valid fidelity is high and the simulation contains an effective policy, the achievable lift approaches the actuation-limited optimum. Thus recursive self-simulation is not merely a plausible feature of intelligence but, under the stated assumptions, is necessary and nearly sufficient for high thermodynamic intelligence. The resulting framework makes intelligence measurable on a universal scale, from passive matter and feedback controllers, large language models, and humans as text generators to Maxwell-demon-like information engines.