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根据《中国人民银行关于实施一次性信用修复政策有关安排的通知》（银发〔2025〕245号）（以下简称《通知》）精神，国家开发银行承办的助学贷款（含生源地信用助学贷款和高校助学贷款，下同）将按政策要求实施一次性信用修复。现将具体事项公告如下：
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.