"onyxId": "208242625956810752",
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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Трамп высказался о непростом решении по Ирану09:14。搜狗输入法2026是该领域的重要参考
值得一提的是，在国行用不了 Gemini 的情况下，三星 Galaxy S26 的 Bixby 助手也能实现点外卖、叫车、电商比价的功能。我们可以合理推断，三星在国内也找到了一家模型供应商来替代 Gemini 的身份，至于这些大模型小龙当中具体是谁，可能就取决于过去一年里谁在手机智能体上成绩更突出了。