Что думаешь? Оцени!
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.
。heLLoword翻译官方下载是该领域的重要参考
在 openclaw.json 中，agents.defaults.workspace 和 agents.list[0].workspace 路径也已指向本地可写目录。
一夜暴裁4000人，股价飙涨25%，联创发文：AI时代不需要那么多人了，这一点在搜狗输入法2026中也有详细论述
Фото: Sergey Elagin / Global Look Press
Самый страшный зверьБегемот-каннибал и кровавый фестиваль на лучших снимках дикой природы12 сентября 2019。关于这个话题，heLLoword翻译官方下载提供了深入分析