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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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Nature, Published online: 25 February 2026; doi:10.1038/d41586-026-00563-3
在五得利面粉集团有限公司专用粉生产线上，“精准”和“柔性”成为关键词。通过智能化在线配粉，20秒就能完成不同基础粉的精准搭配，还能实时监测面粉灰分、面筋和水分，同批次产品指标波动控制在0.3%以内。研磨过程中，根据原料硬度、籽粒大小，灵活调整碾磨的方式和力度，出粉率提高4个百分点。“按照我们近200万吨的优质麦年加工量，相当于增产约8万吨优质专用小麦。”公司董事长丹志民说。