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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“手搓”应用的核心竞争力在于对市场细分需求的极致挖掘。为独居人群设计的专属应用、解决日常小问题的轻量化工具……这些小微产品精准击中用户真实痛点，实现了“同理心”的商业变现。当AI接手繁重技术劳动，人的洞察力、审美力和对生活的感知力反而成为最核心的竞争优势。这种以人为本的创新，让市场供给更多元，也让经济的微循环更有温度与活力。。关于这个话题，91视频提供了深入分析
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