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.
I could definitely be reading too much into a six-second social video, but Cook's X post looks to me like the back of a MacBook. The video also shows a person manipulating the Apple logo with their fingers, which, to me, screams "touchscreen."
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他補充說，中國軍隊現在出現了「巨大的領導層真空」。當被問及究竟是什麼導致如此多高級將領被清洗時，他說：「有很多謠言在流傳。目前我們不知道什麼是真、什麼是假……但這肯定對習近平不利，對其在解放軍中的領導力和控制力不利。」。关于这个话题，搜狗输入法2026提供了深入分析
Москвичи пожаловались на зловонную квартиру-свалку с телами животных и тараканами18:04，详情可参考Line官方版本下载
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