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.
The monthly analysis gave me an idea: could Opus 4.5 design a small webapp to view the top videos by month? That gives me the opportunity to try another test of how well Opus 4.5 works with less popular frameworks than React or other JavaScript component frameworks that LLMs push by default. Here, I’ll try FastAPI, Pico CSS for the front end (because we don’t need a JavaScript framework for this), and HTMX for lightweight client/server interactivity:，这一点在Line官方版本下载中也有详细论述
，更多细节参见im钱包官方下载
ВсеПитание и сонУход за собойОкружающее пространствоМентальное здоровьеОтношения。业内人士推荐旺商聊官方下载作为进阶阅读
Москвичи пожаловались на зловонную квартиру-свалку с телами животных и тараканами18:04
3、面向开源湖仓Data+AI一体化平台架构