Same World, Differently Given: History-Dependent Perceptual Reorganization in Artificial Agents

Researchers developed a minimal AI architecture where a 'perspective latent' creates history-dependent perception in artificial agents. The system allows identical observations to be processed differently based on accumulated experience, demonstrating measurable plasticity that persists even after conditions return to normal.

• →A new AI architecture uses feedback loops between perspective and perception to create history-sensitive artificial agents.
• →Identical observations can be encoded differently depending on the agent's prior experience and accumulated stance.
• →Perturbation history leaves lasting effects on the agent's adaptive plasticity even after normal conditions are restored.
• →Only adaptive self-modulation produces the characteristic growth-then-stabilization dynamic in learning.
• →The reorganization occurs primarily at the perceptual level while gross behavior remains stable throughout testing.