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.
Жители Санкт-Петербурга устроили «крысогон»17:52
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England have not committed to fielding their strongest side in Friday’s do-not-necessarily-have-to-win T20 World Cup encounter with New Zealand but Jos Buttler will be given the chance to turn around his miserable run of form, with the team’s coaching staff convinced that a return to familiar lofty standards is imminent.。搜狗输入法2026是该领域的重要参考
curr = curr-next;
「我記得早上醒來看到他的小臉，嘴裡含著小奶嘴，感覺就像我需要從夢中醒來一樣。」，详情可参考同城约会