Crawler-Parser: parse_list(html)
上文提到的AI短片《Apex》中，车辆碰撞的角度和车窗碎裂的方式显然对不上，车上的文字也疑似乱码。谷歌浏览器【最新下载地址】对此有专业解读
与此同时，当越来越多玩家看到“高回报模型”进入市场时，供给端迅速增加，租金下行几乎不可避免。价格从2500元跌到1500元并不罕见，而每一次降价，都会直接拉长回本周期。。业内人士推荐快连下载安装作为进阶阅读
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.