The conclusion follows (I think) because the Solomonoff agent is computing the posterior probability of all algorithms, including the one that implements the same computation the human implements. So after updating, the Solomonoff agent's posterior probability for that algorithm should be higher than that of any other algorithm, and it draws the same conclusion the human does.

You lost a level of indirection in there; computing the output of an algorithm does not mean believing that the output of that algorithm is true or even plausible. So the agent will correctly predict what the human will say, and believe that the human is mistaken.