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.
There isn’t a ton of innovation when it comes to design in the Wi-Fi extender space. Most of the ones you’ll find today are rounded rectangles roughly the size of your hand that plug into a standard wall outlet. They usually have a few indicator lights that will show you when the extender is connected, how strong its signal strength is and when there’s a problem, and some will even have moveable external antennas that companies claim provide even better Wi-Fi signal. Generally, they are pretty simple to install and get connected, but if you’re struggling with how to set up your Wi-Fi extender, there are plenty of YouTube videos you can check out.
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100多年来，践行初心、担当使命，伟大建党精神融入党的精神血脉，贯穿党的奋斗历程，滋养着、激励着一代代共产党人。