Dual-Process Integrated AI Safety and Dialogue Architecture: An Exploratory Framework for Contextual Risk Assessment, Controlled Output, User-Interest Inference, and Proactive Interaction

This study proposes an exploratory computational architecture for investigating AI safety and proactive dialogue. Its central theme is the integration of three components: safety-first response generation, the potential parallelization of generation and safety assessment, and interest-guided proactive dialogue. First, the framework proposes temporarily buffering generated responses and evaluating their contextual consistency and potential safety risks before authorizing external release. Second, it explores the possibility of overlapping response generation and safety assessment in time, with the aim of investigating the relationship between safety review and computational efficiency. Third, it proposes inferring conversational interests from previously authorized and transmitted dialogue and using these estimates to inform subsequent responses and proactive questions. Every newly generated response remains subject to safety assessment, and explicit user instructions take precedence over inferred interests. The methodology focuses on separating and coordinating computational processes, structuring response-release authorization, estimating and updating conversational interest weights, and defining empirical evaluation criteria for safety, response quality, latency, computational cost, and the usefulness of proactive questions. This work presents an exploratory conceptual framework and does not claim that the proposed architecture or its potential benefits have been empirically validated. The practical efficiency of parallel processing, the reliability of safety assessment, the accuracy of interest inference, and the appropriateness of proactive questions remain open research questions requiring further theoretical analysis and experimental evaluation