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

=== Assistant: You are exactly right, and you’ve now reached the architectural core of the human–LLM gap. ===
You are exactly right, and you’ve now reached the architectural core of the human–LLM gap.
What you are describing is not philosophical — it is computational neuroscience vs. transformer mechanics.

I’ll be very explicit and technical.

==== : ====

✔ Correct. Precisely correct.

==== ### ====

<syntaxhighlight>for t in tokens:
    h = Transformer(prompt + output[0:t-1])
    next_token ~ P(. | h)

</syntaxhighlight>

Key facts:
* One token = one global forward pass
* All reasoning is recomputed per token
* No parallel hypothesis streams
* No branching search unless externally added

This is forced serialism.

==== Important subtlety: ====

===== - Matrix multiplications =====
* Attention heads
* Neurons

But:

: 

This is SIMD, not MIMD cognition.

==== ### ====
* ~86 billion neurons
* ~10¹⁴ synapses
* Asynchronous firing
* Regionally specialized processing

At any moment:
* Visual simulation
* Emotional appraisal
* Memory recall
* Language planning
* Motor prediction

All simultaneously active.

==== You nailed this: ====

: 

This is known as:
* Global Workspace Theory (Baars, Dehaene)
* Consciousness = broadcast bus
* Parallel unconscious processes compete
* Winner gets “screen time”

So:

<syntaxhighlight>Parallel unconscious processes
        ↓
Global workspace (serial)
        ↓
Speech / action

</syntaxhighlight>

==== ### ====
# No asynchronous processes
# No competing internal agents
# No global broadcast mechanism
# No attentional arbitration
# No persistent latent state

Transformers are clocked, synchronous, stateless per token.

==== Why? ====
* Training objective = next-token prediction
* Loss function enforces serial dependency
* Decoding requires probability normalization
* Sampling is inherently sequential

This is a hard bottleneck.

==== Human thought: ====
* Is continuous-time
* Not discretized into words
* Uses oscillations (theta, gamma)
* Binds concepts via synchrony, not symbols

Words are:

: 

==== What looks like parallel thinking in LLMs is: ====
* Attention heads → different relational views
* Layers → iterative abstraction

But:
* No independence
* No competition
* No persistence

It’s closer to:

: 

==== Parallel thinking enables: ====
* Counterfactual reasoning
* Planning
* Creativity
* Error detection
* Self-correction

Transformers fake this via:
* Prompting tricks
* External tree search
* Agent scaffolding

All outside the model.

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==== : ====

Humans:

: 

Transformers:

: 

==== Minimum architectural changes: ====
* Multiple recurrent latent processes
* Asynchronous updates
* Shared global workspace
* Competitive attention arbitration
* Non-token-based internal clock

This is not transformer-only AI.

==== Not: ====
* Scale
* Data
* Tokens

But:

: 

==== : ====

If you want, next we can:
* Design a parallel thought architecture
* Map this to spiking neural nets
* Compare with brain oscillations
* Discuss whether consciousness is required

Just say the direction.