PAUSE: Proactive Appropriateness and Utility for Selective Elicitation

Information-seeking agents must decide not only what information to acquire, but also whether a candidate intervention is appropriate for the current dialogue state. Existing clarification and value-based approaches primarily optimize expected information gain or task utility, implicitly assuming that a valuable intervention is suitable whenever it is selected. We show that this assumption can fail: the same question may be informative but premature at one point in a dialogue and appropriate at another. We introduce PAUSE, a selective elicitation framework that decomposes intervention selection into two decisions: contextual appropriateness and information utility. PAUSE learns an appropriateness score $T(s,R)$ and a utility score $V(s,R)$ from complementary supervision, and performs appropriateness-constrained utility optimization by selecting the highest-value intervention among candidates that satisfy a contextual fit constraint. On NewsInterview, PAUSE improves eight-turn information coverage from 0.5042 with utility-only selection to 0.5676, while leading to more selective clarification behavior on ClariQ. These results suggest that modeling contextual fit as a separate decision factor can improve proactive information seeking, particularly in extended multi-turn interactions.