作为一名长期关注 LLM 架构演进的技术博主，最近发布的 Ring-2.5-1T 引起了我的极大兴趣。不同于市面上常见的 Transformer 变体，它采用了大胆的混合线性注意力架构（Hybrid Linear Attention）。
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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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特点：通过门控机制控制信息流，增强非线性表达。 优点： 适合序列建模、控制性强。 常用于： Transformer FFN、语言模型。
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