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Looking back I must have gotten a pre-edit. But yes this is in-silico not silicon. There is an approach is called neuromorphic architectures and it fits your stated goal better, but the practitioners/researchers belong to a different camp of thought than LLMs (the Bio-inspired camp vs brute force camp)
Uhh learning is not my argument. Maybe I did not make that clear. Amnesiac humans behavior is still not determined solely by current sensory input (Markov property). They may be unable to form new memory but they still possess an internal state(Working memory, Emotional state, Affective valence, a sense of self, a personality) that persists.
If you take two different amnesiac patients with identical sensory input, Same environment, same stimuli, can you confidently predict their actions? Or is there some latent state that is history dependent that influences their behavior?
LLMs don't have an internal state that I know of. If you have another article I'll read it, I do enjoy them.
Its not external vs internal, its integrated vs externally orchestrated. As of right now LLMs do not control memory access, they don't maintain it, and they don't own it. This absolutely could change in the future, I'm not an AGI bear, I am a "LLMs as they currently exist will become AGI" bear.
Then I am mistaken, sorry for attributing an argument to you that is not your own.
https://arxiv.org/abs/2512.23675
https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/
Is merely making LLM weights dynamic at inference enough to challenge your model? KV cache is «external state» but weights must be internal I suppose, since LLMs have already been defined as weights above.
This is all an aesthetics-based argument with arbitrarily drawn categories. I don't see why we should care how particular matrices are stored and multiplied.
This is just giving you state mutation not state persistence. Online learning has been around for a decade+ and unless you want to grant that Alexnet with some online learning was a conscious mind then you argument is also inconsistent.
From an engineering perspective yes, ontologically no. I can shard weights across a gpu, can people shard being across multiple people? I can stream weights from disk, or swap adapters dynamically. That does not magically grant the LLM system intrinsic state.
It doesn't matter from an engineering perspective where a particular set of matrices are stored/operated on but ontologically it matters whether the system has state that persists across interruption and is causally necessary for its future behavior. Its not an arbitrary boundary.
If you pause a human brain and then restart it, it has an internal state that persists, if you do the same to an LLM it loses all internal state, and needs to be externally reloaded to the last checkpoint.
Why? It's a meaningful distinction on its own terms, but what does it have to do with experience, awareness or consciousness?
Because every conscious being we so far have observed to exist is on one side of that boundary. So unless you have a holistic solution to the hard problem of consciousness that can prove that boundary isn't relevant, it points to my original argument.
A lacks necessary conditions for property P, therefore A does not have P. The observed property is that all conscious beings have persistent state that is causally necessary for future behavior. LLMs seem to lack that, so i'd argue they are not conscious
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