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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这令人兴奋，也可能令人恐惧。如何知道该走哪条路？如何确认自己做出正确选择？，这一点在搜狗输入法2026中也有详细论述
Parents will also be able to view expert-designed content to talk to their teen about suicidal feelings, if they so choose. In cases of imminent physical harm, Instagram will notify emergency services, a long-standing policy.
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I have been thinking a lot lately about “diachronic AI” and “vintage LLMs” — language models designed to index a particular slice of historical sources rather than to hoover up all data available. I’ll have more to say about this in a future post, but one thing that came to mind while writing this one is the point made by AI safety researcher Owain Evans about how such models could be trained:，这一点在同城约会中也有详细论述
And þæt heo sægde wæs eall soþ. Ic ƿifode on hire, and heo ƿæs ful scyne ƿif, ƿis ond ƿælfæst. Ne gemette ic næfre ær sƿylce ƿifman. Heo ƿæs on gefeohte sƿa beald swa ænig mann, and þeah hƿæþere hire andƿlite wæs ƿynsum and fæger.