聚合模式进一步放大了这一问题。聚合平台并不直接创造新的交易空间，而是在既有平台之上叠加新的分发层级。每一层抽成都各自合理，但多层叠加后，整体利润空间被显著压缩，使抽佣的边际收益更早触及上限。
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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全国两会召开在即，全国政协委员、广西体育高等专科学校审计与质量管理处处长韦军忙着整理工作笔记，完善提案内容。