In the evolving landscape of data-driven decision-making, existing static knowledge architectures often fail to accommodate real-time nonlinear workflows. This paper introduces , a conceptual framework designed to bridge the gap between structured knowledge bases and adaptive, event-driven processing. JSTARKAN-NEW incorporates three core modules: (1) a dynamic input parser, (2) a recursive relevance evaluator, and (3) a just-in-time synthesis engine. We outline the theoretical foundations, operational principles, and potential application domains. Preliminary analysis suggests that JSTARKAN-NEW can reduce latency in knowledge retrieval tasks by approximately 35–40% compared to traditional static models while maintaining semantic coherence.
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