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.
2026-02-28 00:00:00:0周小霞3014274110http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142741.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142741.html11921 黔北灰豆腐（多味斋）
尤其让我惊艳的，是它在每页备注中生成的演讲词：内容口语化，且熟练使用了「在正式开始之前」、「接下来」等衔接词。这甚至让我感到一丝被硅基生物支配的恐惧：也许未来在台上的某次宣讲中，我们已分不清演讲者是在阐述自己的思想，还是仅仅充当了 AI 的「肉身代言人」。，更多细节参见搜狗输入法2026
async pull(controller: ReadableByteStreamController) {
，更多细节参见搜狗输入法2026
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This formula is satisfiable because if we set to b to true and a to false, then the whole formula is true. All other assignments make the formula false, but it doesn't change that the formula is satisfiable as long as there is at least one assignment makes the formula true.