Do LLMs pass the mirror test?

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Re: Do LLMs pass the mirror test?

#71

Post by post-it » Tue, Jun 30, 2026, 2:32 PM UTC

geon wrote: post #68

post-it wrote: post #30

Earlier quoted context omitted.

> It seems like we forget that LLMs are next token prediction systems. It's pretty clear to me that above a certain size threshold, LLMs are more than a sum of their parts. The sheer amount of training data seems to embed a higher level of reasoning.

No. The reasoning is just an illusion. As soon as you look at it closer, it falls apart completely. https://yosefk.com/blog/llms-arent-world-models.html https://www.reddit.com/r/programming/comments/1mnc9qf/llms_a... https://news.ycombinator.com/item?id=44854518

This is an excellent comment from your HN link:

> The core issue there isn't that the LLM isn't building internal models to represent its world, it's that its world is limited to tokens. Anything not represented in tokens, or token relationships, can't be modeled by the LLM, by definition.

I've noticed that LLMs struggle with tic tac toe, even when I get them to draw it out. And it makes sense! Tic tac toe is an extremely visual game, and difficult to reason about in a single dimension.

Re: Do LLMs pass the mirror test?

#72

Post by tanseydavid » Thu, Jul 02, 2026, 9:51 PM UTC

AlecSchueler wrote: post #63

Charon77 wrote: post #54

Earlier quoted context omitted.

The issue with Markov Chain is you can't get good next token prediction on long enough context because once you see the last 1000 words instead of just 2, it's quite unlikely that your 'frequency' is populated for that exact combination, and markov chain don't work on token embedding that allows some encoding of meaning.

> and markov chain don't work on token embedding that allows some encoding of meaning. Working on an "encoding of meaning" sure sounds a lot like reasoning.

Consider the classic ambiguity example from Natural Language Processing: "Time flies like an arrow; fruit flies like a banana".

Markov chains are mostly about memorized local co-occurrence, while transformers are much better at context-sensitive disambiguation and compositional structure.

I do not have deep knowledge about these subjects but this is what I think was meant by "encoding of meaning" (LLMs). I also do not think this capability has to be thought of as "reasoning".

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