Цены на нефть взлетели до максимума за полгода17:55
Opens in a new window
。关于这个话题，safew官方版本下载提供了深入分析
2024年12月25日 星期三 新京报
2026-02-26 00:00:00:0 审议全国人大常委会工作报告稿等 为召开十四届全国人大四次会议作准备，详情可参考搜狗输入法2026
江波龙作为“存储器第一股”，以PTM商业模式实现差异化突围，覆盖嵌入式存储、固态硬盘等四大产品线，其eMMC和UFS产品全球排名第四，自主研发的主控芯片已批量出货，累计小容量NAND Flash出货量超1亿颗，广泛应用于IoT、汽车、安防等领域。
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.，更多细节参见51吃瓜