# Switch to agent mode
根据IDC的预测，2026年中国智能手机市场上，200美元以下市场份额将缩减4.3个百分点，达到20.0%；200至400美元市场份额34.0%，减少0.3个百分点；400至600美元市场份额10.1%，减少0.8个百分点。高盛分析数据亦显示，中端市场份额自2021年的35%大幅下滑，预计到2027年将降至23%。。业内人士推荐同城约会作为进阶阅读
，更多细节参见同城约会
d01777 0 0 0 /tmp
mkdir -p ~/www/anqicms。业内人士推荐夫子作为进阶阅读
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.