ВВС США призвали Израиль наносить сильные удары по Ирану20:51
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If this is indeed the first full TeX chess engine, it is very unlikely the model memorized one verbatim. That makes TeXCCChess a useful counterpoint to the plausible critique that “coding agents just regurgitate training data”. Of course, the model may have seen discussions about chess programming in TeX, or small macro-expansion tricks, but a full working engine is a different artifact. (We will see in other posts that for engines in more mainstream languages, the pure memorization hypothesis is also questionable given the diversity of architectures and features, but here it is even more so).
n= 513, d= 64, blocks=(128,128) match=True max_abs=0.003483
，这一点在手游中也有详细论述
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.
\nResearchers from Emory University School of Medicine, the University of North Carolina at Chapel Hill, Utah State University and the University of Arizona contributed to the work.。关于这个话题，WhatsApp Web 網頁版登入提供了深入分析