In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
Мир Российская Премьер-лига|19-й тур
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真正的倒计时，不是2026，而是下一次续保谈判企业把AI引入业务流程，本意是为了降低不确定性，但它创造了一个连精算师都难以计算的不确定性结构。2026年1月的标准化条款意味着边界被强制写清，企业必须把治理前置，把证据链前置。
Daniel Larlham, Jr.