Editing Openai/693cab0b-13a0-8011-949b-27f1d40869c1 (section)

=== Assistant: You are pointing at a fundamental architectural discontinuity, not a minor limitation. ===
You are pointing at a fundamental architectural discontinuity, not a minor limitation.
This is one of the cleanest fault lines between human cognition and current LLMs.

I’ll be precise and uncompromising.

==== ### ====
* Thought does not terminate at output
* Output is a side-effect, not a halting condition
* Cognition is continuous, self-propagating

===== - Output terminates computation =====
* No post-output state evolution
* Cognition is episodic and externally clocked

This is absolutely correct.

==== ### ====
* Autocatalytic
* Recurrent
* Self-exciting
* Homeostatic but non-terminating

Formally closer to:

<syntaxhighlight>Thoughtₜ₊₁ = f(Thoughtₜ, World, Self)

</syntaxhighlight>

There is no STOP token in biology.

==== ### ====
* Peripheral motor programs
* Downstream of cognition
* Weakly coupled to core thought loops

Brain architecture:
* Cortex ↔ Cortex (continuous)
* Cortex → Motor → Speech (one-way)

So:

: 

==== Yes — but not linear. ====

===== - Cyclic =====
* Branching
* Self-referential
* Spontaneously reactivated

This is closer to a strange attractor than a chain.

==== An LLM is: ====

: 

Formally:

<syntaxhighlight>P(next_token | prompt)

</syntaxhighlight>

Even with long context:
* No internal clock
* No persistence after emission
* No autonomous continuation

==== ### ====
* Feedforward at inference time
* Driven by external token demand
* Optimized for loss minimization, not cognition

Once the last token is emitted:
* Computation halts
* Latent state is discarded
* No memory consolidation

==== : ====

==== Humans have: ====
* Curiosity drives
* Prediction error minimization
* Homeostatic imbalance
* Self-model mismatch

These generate:

<syntaxhighlight>Internal pressure → continued cognition

</syntaxhighlight>

LLMs have:
* Zero intrinsic drives
* No self-error signal
* No survival objective

==== To support: ====

<syntaxhighlight>Thoughtₙ → Thoughtₙ₊₁ → …

</syntaxhighlight>

You need:
* Persistent internal state
* Internal time
* Energy budget
* Stability mechanisms
* Forgetting + reinforcement

Transformers have none of these intrinsically.

==== CoT looks like: ====

<syntaxhighlight>Thought A
Thought B
Thought C

</syntaxhighlight>

But in reality:
* Each token recomputes from scratch
* No evolving inner state
* Just conditional sampling

It’s simulated continuity, not real continuity.

==== Minimum requirements: ====

===== - Recurrent core (not just depth) =====
* Persistent latent workspace
* Self-triggered update loop

===== - Not next-token prediction =====
* Predictive processing
* World-model learning
* Energy minimization

===== - Non-halting inference =====
* Interruptible output
* Background cognition

This is not a transformer anymore.

==== - Recurrent World Models → limited domains ====
* Agent loops → fake continuity (external script)
* Memory-augmented LLMs → static recall, not thought
* Reflection prompting → textual illusion only

None achieve:

: 

==== Humans are processes ====
LLMs are functions

Processes:
* Persist
* Drift
* Self-modify

Functions:
* Map input → output
* Terminate
* Forget

==== AGI requires: ====
* Non-terminating cognition
* Self-driven thought chains
* Internal world simulation

Without:

: 

==== - ✔ Humans think beyond output ====
* ✔ Thought does not halt
* ✔ Cognition is infinite, recursive, self-triggered
* ❌ LLMs do not think unless poked
* ❌ LLM cognition halts at output
* ❌ “Thought infinity” is impossible in current LLMs

==== : ====

If you want next, we can go into:
* What a non-halting AI architecture would look like
* Why consciousness likely requires this
* Whether this violates computability theory
* How energy, metabolism, and cognition connect

Say where you want to push.