近日，一车主驾驶领克Z20夜间在高速上行驶时，语音操作误关大灯致车辆撞上护栏一事引发网友广泛关注。针对此事，领克汽车销售有限公司副总经理穆军通过社交平台进行回应。2月26日，穆军在其个人微博发文称：“昨晚发生一起领克Z20车辆行驶中语音误操作控制关闭大灯的情况，今天我们第一时间完成了语音控制优化方案，现已通过云端推送更新，后续在行驶状态下只能通过手动控制大灯关闭，请大家放心。感谢用户的反馈与监督，对此带来的困扰我们深表歉意，领克始终守护您的安全。”（财联社）
Failure of Safe Defaults: The default state of a generated key via the GCP API panel permits access to the sensitive Gemini API (assuming it’s enabled). A user creating a key for a map widget is unknowingly generating a credential capable of administrative actions.
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（三）在铁路、城市轨道交通线路、桥梁、隧道、涵洞处挖掘坑穴、采石取沙的；
值得一提的是，因内存价格影响，英伟达的 AI 计算机 DGX Spark 宣布涨价 700 美元（目前售价 4699 元，约合人民币 32229 元），并且本次涨价将适用于所有地区。
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.