ResearchSpec: Explicit Semantic State for Grounded Answering and Clarification

Language-model assistants must decide whether available information warrants a substantive answer, clarification, or an acknowledgment of insufficiency. Information sufficiency depends on which task requirements the context resolves. We introduce ResearchSpec, an explicit semantic state framework that records task conditions by necessity, status, source evidence, and authorization scope. It maintains conditions across turns, generates responses using them with the original context, and assesses the condition representation and each candidate's support and coverage. The framework requires no additional task-specific training; assessment reports inform subsequent responses when interaction continues. Across 13,872 tasks, the complete ResearchSpec procedure improves the selected primary metrics over direct generation with the same backbone by 14.09, 3.37, 15.39, and 3.68 percentage points on MiP, FaithEval, ClarifyMT, and SciConvBench, respectively. Across 634 MiP source pairs, joint success in answering the original problem and abstaining after premise removal rises from 45.11% to 69.40%. On a separate 55-task interactive cohort, incremental maintenance has 25.89% lower estimated solver cost than full-dialogue reconstruction; both arms have the same observed point estimates for grounded and final resolution. These findings support the complete condition-based procedure for adapting responses to available information and reusing established requirements, with additional inference cost and task-dependent completion tradeoffs.