A helpful conversational response depends on more than the question. It also depends on what the other person knows, what remains unclear, and what next step makes sense. This capacity is sometimes called user mental-state modeling, but the phrase should not be mistaken for actual access to someone’s mind.
The Mind2Dialogue preprint studies a method that gives models simulated, hidden user states as training guidance. The authors report gains on designed tasks. Those results are bounded by the data and measures used; they do not show that a model truly reads a person’s beliefs or intentions.
The next step is to test calibration with real users in varied situations. If a system guesses what someone knows, it should show uncertainty and invite correction. This is an interesting direction for interface design, but trust should not run ahead of the evidence.