Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Yes — exactly! ===
Yes — exactly! Now we’re getting to the right intuition. Let me explain carefully.

==== - Each neuron passes a signal along its connections, weighted by the connection strength (weight). ====
* By the time the model reaches the final output layer, each possible token has a “score” based on how strongly all neurons collectively support it.
* Lower temperature = the AI almost always picks the token with the highest score, ignoring weaker signals.
* Higher temperature = the AI allows weaker signals to compete, sometimes picking less-likely tokens.

: 

==== - The network computes all activations normally, so all neuron outputs are available. ====
* Temperature doesn’t change these outputs; it changes how the final outputs (tokens) are sampled from those outputs.
* Essentially: temperature selectively amplifies or suppresses weak “suggestions” from the network.

==== - Neuron activations and weights never change. ====
* What changes is which “path through the network” dominates the final choice: - Low temperature → strongest, most consistent path wins → deterministic output - High temperature → weaker paths can win sometimes → creative / varied output

: 

If you want, I can make a diagram showing neurons sending signals through paths, and how low vs high temperature affects which path is chosen — it makes this idea very concrete.

Do you want me to do that?