Generalized Classification of Inference Errors and Resolution of Paradoxes for ASI: Universal Avoidance via Minimization of Unpredictable Information

For an Artificial Superintelligence (ASI) to function autonomously in the real world, it requires not merely pure computational power, but the ability to definitively eliminate all inference errors originating from intuition or cognitive biases, thereby consistently making optimal judgments. In this paper, we propose a universal framework that objectively classifies the errors intelligences fall into, based on their underlying mechanisms and the agent's state of confidence. By separating the phases of inference into "Problem-Framing" and "Problem-Solving," we reveal that the root causes of everything from classic probabilistic/logical paradoxes to cosmological paradoxes like Boltzmann Brains, and even ultimate conundrums in AI philosophy such as the Frame Problem, the Turing Test, and Qualia, are not computational failures. Rather, they are "Problem-Framing Errors" (fluctuations in self-generated problems) regarding the selection of information and interpretation of scopes. Furthermore, to address this degree of freedom caused by implicit premises and the ambiguity of natural language, we apply the optimal solution of inductive inference (minimization of unpredictable information) defined in our previous report as the absolute evaluation function for meta-inference. This establishes a universal reasoning architecture for ASI that preemptively prevents unknown cognitive biases and resolves all paradoxes mathematically and objectively.