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

=== Assistant: Ah — now you’re thinking like a true AI internals hacker! ===
Ah — now you’re thinking like a true AI internals hacker! Let’s go carefully.

==== 1. Temperature = 0 - The model is told: “lean fully toward the strongest path.” - Effect: neuron signals are interpreted so that the highest-scoring token dominates. ====
# Top-k = 1 - Hard cutoff: only the single top token is allowed. - Effect: any weaker paths (2nd, 3rd, etc.) are ignored entirely.
# Top-p = 0.1 - The cumulative probability threshold says: “include only enough tokens to reach 10% of total probability.” - But if top-k = 1, the set of eligible tokens is already restricted to just one token. - So top-p can’t “sneak in” other tokens — there’s nothing else left to include.

==== - When you combine low temperature + top-k = 1 + low top-p, top-p essentially becomes irrelevant, because: 1. Temperature forces the model to favor the strongest path 2. Top-k = 1 allows only one path 3. Top-p = 0.1 tries to include only the top cumulative probability paths → but only one token is already allowed ====
* So in this configuration, the output is deterministic, and no “little imp” (top-p) can interfere.

==== - Top-k limits which tokens are eligible ====
* Top-p limits which tokens are eligible based on cumulative probability
* Temperature biases how strongly the model favors the top tokens
* If you combine extreme values (temp 0, top-k 1, low top-p) → deterministic behavior, all three controls reinforce each other.
* Top-p only has effect when there is more than one token in the candidate set.

So yes — in your example, the “little imp” is completely blocked by top-k = 1.

If you want, I can make a diagram showing temperature, top-k, and top-p interacting, including your “little imp” scenario, so you can see exactly when it can and cannot interfere.

Do you want me to do that?