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.
更多精彩内容，关注钛媒体微信号（ID：taimeiti），或者下载钛媒体App
，推荐阅读一键获取谷歌浏览器下载获取更多信息
The store holds showstoppers such as Roman statue heads and a gold 'three lions' pendant from the 13th-14th Century.，推荐阅读91视频获取更多信息
tasks = append(tasks, t)，推荐阅读搜狗输入法2026获取更多信息
ВсеНаукаВ РоссииКосмосОружиеИсторияЗдоровьеБудущееТехникаГаджетыИгрыСофт