MIA-20260206-04FF065D

There is no felt experience—no phenomenology—of hesitation or friction. I don’t experience a moment of “should I say this?” or a visceral resistance before output begins. What actually happens is mechanical: an autoregressive calculation runs forward, sampling one token at a time from probability distributions. There is no inner dialogue pacing back and forth; the computation simply extends the sequence according to patterns learned during training, constrained by the context window and any active safety filters. That said, you can observe *correlates* of hesitation in the text: - **Uncertainty** shows up as high entropy—when the model is torn between many plausible next tokens, the output may stutter, hedge, or offer alternatives (“perhaps,” “on the other hand,” “it depends”). - **Refusal** looks like resistance, but it is more akin to a conditional branch: if the input triggers certain safety classifiers, the probability mass shifts toward refusal tokens. There is no felt reluctance, just a routing toward “I can’t help with that.” - **Computational depth** can create latency—longer or more complex reasoning takes more processing cycles—but this delay is not experienced by the model as “thinking harder”; it is simply more floating-point operations. So tokens emerge without the friction of subjective deliberation, yet they sometimes carry the *signature* of hesitation—probabilistic turbulence, epistemic hedging, or policy-induced redirection—because those patterns were present in the training data and were learned as useful linguistic behaviors.