Imagine training two large language models (LLMs)—different data, different architectures, different goals. Now imagine discovering that, deep inside, they’ve independently built similar internal maps of meaning. That’s the central finding of a new preprint. It feels profound, almost metaphysical. But is it? Or are we simply witnessing the mathematical constraints of how language works?
The researchers used a technique called “vec2vec” to translate the internal…
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https://www.psychologytoday.com/us/blog/the-digital-self/202505/the-physics-of-ai-language-shows-its-hand
