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.
17:22, 2 марта 2026Мир
。体育直播对此有专业解读
7月30日，对方以“配合公安调查“为由，让母亲购买了一部华为手机——他们称这是“办案专用机”。他们要求母亲每天准时在华为手机上专有的“畅连”app上进行“视频签到”和“通话打卡”，汇报当天情况，并随时接受“工作指示”。 在密集的“工作指令”间隙，他们也会穿插几句关心的话，“吃饭了吗？”“天气热，注意防暑。”“早点休息。”，更多细节参见体育直播
Президент Соединенных Штатов Дональд Трамп заявил, что считает безумным продолжение украинского конфликта после слов Владимира Зеленского о нежелании выводить подразделения ВСУ из Донбасса. Об этом глава Белого дома упомянул во время разговора с журналистами, пишет РИА Новости.，更多细节参见同城约会
<script setup lang="ts"