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.
В Финляндии предупредили об опасном шаге ЕС против России09:28。搜狗输入法2026是该领域的重要参考
Copyright © 1997-2026 by www.people.com.cn all rights reserved，更多细节参见雷电模拟器官方版本下载
表妹挨打的理由，往往只是些无关紧要的小事。比如，外公煮面时，把火腿肠连带外包装扔进锅里煮，她不想吃，挨打；抽背九九乘法表，被问到老师没教过的部分，背不出，挨打。继承了外公脾气的舅舅，每次回家，教育孩子的方式也如出一辙。
Alexey Milovidov Co-founder & CTO, ClickHouse