スズキ・鈴木俊宏社長「社員の主体性引き出す組織づくりとは」
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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For content creators, this creates both opportunities and challenges. The opportunity is that appearing in AI-generated responses places your content in a prominent, trusted position that provides context and drives qualified traffic. The challenge is that optimization strategies must adapt to capture this visibility. Content that ranks well in traditional search results won't automatically appear in AI Mode responses without deliberate optimization for how AI systems evaluate and select sources.
据小德介绍，700公里的路程，他的智驾里程达到了512.2公里，路程占比高达76%。“长途驾驶，智能驾驶确实会缓解司机的疲劳，这不还能为智驾提供数据基础嘛。”小德颇为幽默地说道。