Мерц резко сменил риторику во время встречи в Китае09:25
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在推进“再军事化”进程中，日本右翼的拥核野心日益膨胀。日本政客近期不断进行危险试探，公开鼓噪“拥核”，谋求修改长期奉行的“无核三原则”。众所周知，日本是典型的“核门槛国家”，长期制造、囤积远超民用核能实际需求的钚材料。截至2024年底，日本囤积的分离钚材料总量已高达44.4吨。日本现已建成完整的核燃料循环体系，具备较强的核工业能力，能够依托核反应堆和后处理技术及设施生产武器级钚材料。一旦右翼的政治狂热驱动日本迈过“核门槛”，潘多拉的魔盒将被打开，全球核不扩散体系将遭到严重冲击。新加坡《联合早报》不久前刊发评论指出，核不扩散仍然是当今国际政治的主流旋律，日本拥核的走向不符合东南亚利益，降低军备竞赛和减少战争风险，才符合区域稳定之需要。，详情可参考快连下载安装
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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.
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