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.
Altman 把人类成长比作「训练」，听起来像把人当机器。这让很多人不爽，觉得贬低了人类的价值——生命不是数据输入输出啊！
00:22, 28 февраля 2026Мир。搜狗输入法2026是该领域的重要参考
第四十四条 社区综合服务设施建设应当纳入相关规划，由县级以上地方人民政府统筹解决。
。业内人士推荐服务器推荐作为进阶阅读
09:14, 28 февраля 2026Мир
Weathering challenges，这一点在搜狗输入法下载中也有详细论述