[mrob]

It's plausible that LLMs experience things during training, but during inference an LLM is equivalent to a lookup table. An LLM is a pure function mapping a list of tokens to a set of token probabilities. It needs to be connected to a sampler to make it "chat", and each token of that chat is calculated separately (barring caching, which is an implementation detail that only affects performance). There is no internal state.

[stratos123]

The context is state. This is especially noticable for thinking models, which can emit tens of thousands of CoT tokens solving a problem. I'm guessing you're arguing that since LLMs "experience time discretely" (from every pass exactly one token is sampled, which gets appended to the current context), they can't have experiences. I don't think this argument holds - for example, it would mean a simulated human brain may or may not have experiences depending on technical details of how you simulate them, even though those ways produce exactly the same simulation.

[mrob]

The context is the simulated world, not the internal state. It can be freely edited without the LLM experiencing anything. The LLM itself never changes except during training (where I concede it could possibly be conscious, although I personally think that's unlikely).

[Kim_Bruning]

Right, no hidden internal state. Exactly. There's 0. And the weights are sitting there statically, which is absolutely true.

But my current favorite frontier model has this 1 million token mutable state just sitting there. Holding natural language. Which as we know can encode emotions. (Which I imagine you might demonstrate on reading my words, and then wisely temper in your reply)