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.
But that still amounted to about 40,000 people.。关于这个话题，爱思助手下载最新版本提供了深入分析
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对于2025年11月30日（含）前已足额偿还的助学贷款逾期本息，征信报告将于2026年1月1日起不予展示相关逾期信息；对于2025年12月1日至2026年3月31日（含）之间足额偿还的助学贷款逾期本息，征信报告将于还款次月月底前不予展示逾期信息。，这一点在同城约会中也有详细论述
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