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.
如果类比 iPhone 的成功经验，这可能就是 AI 硬件的「多点触控」。
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It sounds like a contradiction, but for Carroll, it's the difference between destiny and graft.
一、从无到有：Sun City医疗体系的进化之路Sun City 由Del Webb公司创办，定位是“活跃退休人士社区”。但它的医疗服务，并非一开始就配齐，而是跟着居民需求，一步步迭代升级，大致分为四个阶段。