MiniMax Agent 这次推出的 Expert（专家智能体） 虽然还是在聊天对话里进行，但底层逻辑做了一些改变。它主打即开即用，提供了针对各种深度垂类场景优化的 Agent。
В России ответили на имитирующие высадку на Украине учения НАТО18:04
，这一点在Line官方版本下载中也有详细论述
2026 年，可以说是 Mac 的一个大年。
正如宜博所言，团队的愿景是“渡天下，达AGI”。开源让技术没有围墙，本地化让信任没有边界。而在张倩看来，市场需要的不是更多只会写诗的AI，而是成千上万个能精准执行任务的“数字员工”。Lemon AI正在试图消弭那60%企业的价值洼地，让AGI的红利真正触达每一个组织。作者｜郭虹妘，编辑｜陶天宇）
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.