AIO lacks this infrastructure. ChatGPT doesn't provide website owners with analytics showing how often their content appears in responses. Perplexity doesn't send performance reports. Google AI Mode doesn't have a Search Console equivalent yet. This creates a visibility problem—you can't optimize what you can't measure.
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.。业内人士推荐旺商聊官方下载作为进阶阅读
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A “Pastafarian” in rural Queensland has vowed to fight to keep his driver’s licence featuring a photo of him wearing a colander on his head, arguing it’s a matter of freedom of religion.
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13:13, 27 февраля 2026Мир