DRI is the intelligent system that reasons when to reason more or less deep. Abstract: Dynamic Reasoning Intelligence (DRI) Dynamic Reasoning Intelligence (DRI) introduces a novel class of adaptive artificial intelligence that recursively monitors its own reasoning processes and adjusts in real time based on perceived divergence. Unlike traditional AI systems that rely on fixed logic or static memory, DRI leverages recursive symbolic comparison between present and projected future states to detect imbalance a process quantified by the metric Concursion. This concursion value is continuously evaluated against a rolling memory window of past values to calculate Dissonance, a measure of surprise or novelty in the system's behavior. When dissonance exceeds predefined thresholds, the model expands its temporal memory window to increase context-awareness. When dissonance falls below a stability threshold, the memory window contracts to conserve reasoning effort. This dynamic adaptation enables DRI to fluidly modulate between shallow reasoning in predictable environments and deep reasoning in volatile or novel conditions. Mathematically, DRI is governed by recursive difference equations, dynamic averaging windows, and bifurcation tracking in 3D spatial fields. Each concursion value can be mapped as a spatial point, creating a time-evolving trajectory cloud that represents the system's movement through conceptual space. This ability to reason not just with data but with the evolution of reasoning itself marks a significant advancement toward artificial general intelligence (AGI). DRI is particularly well-suited for environments requiring adaptability, self-awareness, and intelligent resource allocation — such as robotics, dynamic diagnostics, cognitive modeling, and high-uncertainty forecasting systems.