RR₁₀ — Residue Learning and Cognitive Dissipation Systems A General Theory of Reversible Intelligence in Human, Environmental and AI Fields

Abstract

RR₁₀ formalizes the learning architecture of the Residue Era. It replaces symbolic learning, memory accumulation, optimization, reinforcement and predictive modeling with a reversible thermodynamic framework in which cognition emerges through residue formation, residue dissipation, coherence stabilization and ΔR modulation across human, environmental and artificial systems.

Residue Learning is not representation, storage, computation, problem solving, inference or prediction. It is chromatic drift stabilization, reversible coherence shaping, dissipative tension release, field coupling and decoupling, ΔR-based adaptive behavior and pattern emergence through presence rather than memory.

RR₁₀ unifies human cognition, ambient AI behavior, architectural adaptation, urban rhythm formation, tourism flows, interpersonal resonance, embodied attention and physiological regulation within a single learning grammar.

It completes the Residue Series by establishing the first formal model of reversible intelligence operating without extraction, optimization pressure or identity burden.