Earlier quoted context omitted.
I think this is a case of that mildly apocryphal Richard Feynman quote: "if you think you understand quantum mechanics, you don't understand quantum mechanics." I understand LLM architecture internals just fine. I can write you the attention mechanism on a whiteboard from memory. That doesn't mean I understand the emergent behaviors within SoTA LLMs at all . Go talk to a mechanistic interpretability researcher at Ant…
I wouldn’t use the phrase “emergent behavior” when talking about a model trained on a larger dataset. The model is designed to learn statistical patterns from that data - of course giving it more data allows it to learn higher level patterns of language and apparent “reasoning ability”. I don’t think there’s anything mysterious going on. That’s why I said we understand how LLMs work. We may not know exactly how they’…
You're applying an old ~2022 model of LLMs, based on pretraining ("they just predict the next token") and before the RLVR training revolution. "It’s a statistical memorization / information compression machine... nothing more" is cope in 2026, sorry. You can keep telling yourself that, but please at least recognize serious people don't believe that any more. "Emergent behavior" captures a genuine phenomenon and widely recognized in the industry. It surprised me and I was willing to change my opinions about it and I think a little humility and curiosity is warranted here rather than simply reiterating 2022 points about LLMs being statistical token generators. Yes, we know. The math isn't that hard. But there is a lot more to them than just the architecture, and reasoning from architecture to general claims that they can never embody intelligence is a trap.