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.
具体来看，Qwen3.5 采用混合注意力机制，结合高稀疏的 MoE 架构创新，并基于更大规模的文本和视觉混合 Token 上训练，Qwen3.5-122B-A10B 与 Qwen3.5-35B-A3B 以更小的总参数和激活参数量，实现了更大的性能提升。
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