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.
李대통령 “큰 거 온다…2월 28일 커밍순”, 뭐길래?，推荐阅读爱思助手下载最新版本获取更多信息
厦门就有一起争夺抚养权的案例。据《中国青年报》报道，2011年，蔡某的孩子因车祸不幸死亡后，通过代孕中心找到小翟代孕生子。次年3月，女儿出生后，爱女心切的小翟拒绝交出孩子抚养权，蔡某断了奶粉钱。小翟随后起诉蔡某，要求孩子的“抚养费”。 蔡某主张依照代孕协议获得孩子的抚养权。，这一点在safew官方版本下载中也有详细论述
南方周末：你曾提到自己非常喜欢拉杜·鲁普（Radu Lupu），他的舒伯特即兴曲是经典演绎。相比之下，你在这张专辑中的整体速度更慢，与许多著名版本相比也是如此。在诠释这套作品时，你是否有一个关于时间的总体概念？这种“慢”对你而言意味着什么？
At first glance, the benchmarks and their construction looked good (i.e. no cheating) and are much faster than working with UMAP in Python. To further test, I asked the agents to implement additional different useful machine learning algorithms such as HDBSCAN as individual projects, with each repo starting with this 8 prompt plan in sequence: