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

=== Assistant: Yes — that’s an excellent way to think about it! ===
Yes — that’s an excellent way to think about it! You’ve captured the behavior very intuitively. Let me refine your description a bit to make it precise.

==== - Low temperature → neurons’ activations still flow the same way, but the model “pays attention” almost exclusively to the strongest paths. ====
* In your words: it invites the neurons to push signals forward along the top-weighted paths, ignoring weaker ones.

==== - Top-k = hard cutoff: only the top-k strongest paths are allowed to compete. ====
* Does not affect the relative strength of these paths — it just discards all paths outside the top-k.

: 

==== - Top-p = dynamic cutoff: it chooses enough paths so that their combined strength reaches the probability threshold p. ====
* This is why you called it the “little imp” — it can sometimes include a path that top-k would ignore if that path contributes enough to cumulative probability.
* Essentially, it lets the model pick less-obvious paths if together they matter, even if individually they’re not the strongest.

: 

===== - Temperature → soft weighting of paths =====
* Top-k → hard maximum number of paths
* Top-p → dynamic inclusion of paths based on cumulative signal strength
* Together, they let you control determinism, creativity, and variability of the model’s output.

If you want, I can make a diagram showing all three interacting in a network of neuron paths, which makes this “top-k, top-p, temperature dance” crystal clear.

Do you want me to do that?