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.
“Breakfast is a vector space. You can place pancakes, crepes, and scrambled eggs on a simplex where the variables are the ratios between milk, eggs, and flour. We have explored too little of this manifold. More breakfasts can exist than we have known.”
。业内人士推荐夫子作为进阶阅读
Мощный удар Израиля по Ирану попал на видео09:41
There used to be countless companies making flagship Android phones, but a combination of factors has narrowed the field over time. Today, Samsung is the undisputed king of the Android device ecosystem with its Galaxy S line. So we can safely assume today's Unpacked has revealed the most popular Android phones for the next year—the Galaxy S26 Ultra, Galaxy S26+, and Galaxy S26.