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This paper proves two necessary conditions for optimal inference in a mesh of sovereign agents with irregular, non-stationary observations: an adaptive timescale and gap-dependence, which are satisfied only by liquid (continuous-time) networks.
MindZero introduces a self-supervised reinforcement learning framework that trains multimodal large language models for efficient and robust online mental reasoning without requiring mental state annotations, outperforming model-based methods in accuracy and efficiency.