"She smashed it," he noted.
// 易错点2：漏写stack.length判断 → 栈空时访问stack[-1]会报错
，更多细节参见搜狗输入法2026
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2026-02-27 00:00:00:03014251610http://paper.people.com.cn/rmrb/pc/content/202602/27/content_30142516.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/27/content_30142516.html11921 一版责编：杨 旭 赵 政 张宇杰 二版责编：殷新宇 张安宇 崔 斌 三版责编：韩晓明 姜 波 程是颉 四版责编：袁振喜 陈 震 余 璇
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.