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.
"This Lifetime Achievement Award recognises a remarkable legacy built on originality and enduring influence, that continues to connect with fans worldwide."
，详情可参考heLLoword翻译官方下载
同时，消费者的需求也在向健康升级，少盐、少油、低脂成为主流。但目前餐饮市场中，品牌对用户的细分需求并不重视，我们并没有为用户解决更多问题。例如，针对甲亢患者的低碘餐、对糖尿病患者友好的餐食，这类细分需求市场，几乎处于空白状态。
Publication date: 28 February 2026
Supports the critical but underfunded open source projects that conventional models miss.