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在产品路径上，刘强东的思路与中国新能源汽车的换道超车有异曲同工之处：明确主打新能源智能化游艇，强调通过深度应用AI与机器人技术，打造更安全、智能的产品，以“绿色”与“智造”重做游艇，而不是简单复制传统豪华游艇模式。
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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.
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