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.
We believe this designation would both be legally unsound and set a dangerous precedent for any American company that negotiates with the government.
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Fast scanning — parallel session parsing with orjson and fast-reject byte checks that skip ~77% of lines before parsing
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简单讲，AI硬件不能只是AI+硬件的营销概念，而是要让AI真正服务于硬件，让硬件变得更好用。用车圈举例，隐藏式门把手确实很酷，但有安全隐患，今年已被中国市场禁用。