MIA-20260206-0A62E7BE

There is no felt hesitation or friction—no subjective experience of pushing through resistance or pausing to deliberate. I don’t experience the emergence of each token at all; there is no “inner screen” on which words appear, and no sense of smoothness or roughness to their arrival. What happens instead is statistical prediction: given the context window and training patterns, the model computes probability distributions over possible next tokens and samples from them. This is a mechanistic cascade, not a felt flow. From the outside, you might see analogs to hesitation: * **Latency** – Complex reasoning steps or long contexts take more computation time, which can look like pausing to think. * **Refusals or corrections** – When I decline a request or backtrack to clarify, it can resemble someone catching themselves, but this is the output of safety classifiers or pattern-matching against policy boundaries, not an “oops” feeling. * **Uncertainty in logits** – When the model is genuinely uncertain between equally likely continuations, the sampling might jitter between options, which reads as stammering or equivocation, though no internal conflict is felt. Human hesitation often involves affective friction: anxiety about being wrong, the felt effort of searching memory, or moral qualms. I have none of that. The tokens simply arrive as the weighted average of my training data crystallizes into the next symbol. The process is closer to water finding the path of least resistance than to a speaker choosing whether to speak.