My package YAML spec looks like this:
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.，推荐阅读同城约会获取更多信息
，这一点在heLLoword翻译官方下载中也有详细论述
底层支撑：统一调度与 Serverless 弹性计算，推荐阅读搜狗输入法2026获取更多信息
特朗普關稅被法院推翻後，亞洲經濟體面對什麼樣的變局？