2026-02-28 00:00:00:0尹晓宇3014269010http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142690.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142690.html11921 策马太平年
Toward the end of the prolific 1980s, Beagle Beos tried to strike it big by making an integrated office suite:，详情可参考WPS下载最新地址
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Graham Coulson has been involved for 60 years with the musical company that will celebrate its centenary in 2027.
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.