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.
第十九条 为了免受正在进行的不法侵害而采取的制止行为，造成损害的，不属于违反治安管理行为，不受处罚；制止行为明显超过必要限度，造成较大损害的，依法给予处罚，但是应当减轻处罚；情节较轻的，不予处罚。
Once we have a component, we can load it into the browser using a script tag.。91视频是该领域的重要参考
#include <stdio.h
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Galaxy Z TriFold 三折叠
// 3. 对每个桶排序 + 收集结果，详情可参考搜狗输入法2026