; Step 3a: Same-privilege (PLA returned 0x000 = continue)
从2026年1月开始，AI风险到底算不算承保范围将被保险业写进条款。Verisk推动的AGI排除背书以2026年1月开始生效，把一块长期模糊的责任边界变成行业文本。
这种定位通过纪录片创作得以深化。剪辑陷入瓶颈时，他的导师提供了颠覆性的建议：关掉所有画面，只聆听采访录音，两个月内不看影像。这对习惯于视觉思维的创作者而言，无异于一次“信仰的飞跃”。他照做了，两个月里，他只面对亲人们的声音。那些用粤语、英语讲述的，充满情感风暴、时常跳跃、夹杂着痛苦与怨愤的叙述，动荡时期的恐惧、逃亡路上的艰辛、家庭内部的委屈，所有这些情绪，剥离了画面的修饰，以最直接的声音形式冲击着他。，推荐阅读快连下载安装获取更多信息
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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.