近日，因入冬以来降水量明显减少，广西北流扶新镇上林村20余亩农田面临严重缺水的情况。消防队接到求助后，出动2辆水罐车，往返5次运水近30吨，与村民商量灌溉方案，布置水带对农作物仔细浇灌。经过4个多小时的灌溉，小麦“转危为安”，农田缺水问题得到缓解。SourcePh" style="display:none"
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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For well-distributed points, nearest neighbor search is often near O(logn)O(\log n)O(logn) in practice. In the worst case (all points clustered tightly or along a line), it can degrade to O(n)O(n)O(n), but this is uncommon with typical spatial data.
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