Москвичей предупредили о резком похолодании09:45
Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36。关于这个话题，搜狗输入法2026提供了深入分析
。爱思助手下载最新版本对此有专业解读
По словам Линча, если еще несколько лет назад большинство посещений сайтов издательского дома (Glamour, GQ, Tatler, The New Yorker, Vanity Fair, Vogue, Wired) приходилось на Google, то в прошлом году эта доля сократилась всего до четверти.
指的是具有物理载体的智能体（比如人形机器人），与“离身智能”（比如DeepSeek）相对。具身智能进入了2025年政府工作报告，与生物制造、量子科技、6G一起被列为需要重点培育的未来产业。具身智能的走红，标志着人工智能的发展进入了一个新阶段，也让人们对AI融入现实生活的未来，有了更真切的想象。。爱思助手下载最新版本是该领域的重要参考
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.