这篇arXiv论文用数学分析AI自我改进的爆炸极限，指出生成时间比想象中更关键，和以往经济模型结论不同。
这篇论文发表于arXiv（2608.14426），研究AI参与自身研发的反馈循环可能引发的智能爆炸。作者发现，迈向垂直渐近线的奇异增长比以往经济学启发模型预期的更难实现。论文指出存在一类比指数更快但不会导致垂直渐近线的增长率，这被此前研究忽略。生成时间，即完成一轮反馈循环所需时间，被证明是决定爆炸行为的关键参数。除非生成时间迅速趋近于零，否则无法实现奇异增长。
The Dynamics of Intelligence Explosions
AI is increasingly being used to help with AI R&D. Under certain conditions this feedback loop might be able to produce an intelligence explosion, with rapidly escalating AI capabilities. I explore the mathematics of the most explosive possibilities, with an eye to understanding what drives the dynamics. I show that singular growth (towards a vertical asymptote) is harder to achieve than would be expected from recent economics-inspired modelling, and that there is an important but neglected class of growth rates that are faster than exponential but don't lead to a vertical asymptote. I draw out the generation time (the time to go around the feedback loop) as a neglected parameter that plays a pivotal role in determining the behaviour of any intelligence explosion --- one cannot have singular growth unless the generation time rapidly approaches zero.