Viridis Canon — Series S6: Cognition & Learning (v1.0.0)
Year: 2026
Type: preprint
Abstract: Viridis Canon — Series S6: Cognition & Learning. The thermodynamic bounds and optimal schedules governing how an intelligence acquires and generalizes information — the Intelligence Bound self-applied to cognition and learning. Founding members: the Cognitive Throughput Theorem (CTT), the Dissipative Generalization Theorem (DGT, ‘the Annealer’), the Harmonized Descent Theorem (HDT, ‘the Learner’), and the Mutualistic Entropy-driven Learning Theorem (MELT). Each theorem is machine-checked in Lean 4 via Aristotle (zero sorry, axiom audit ⊆ {propext, Classical.choice, Quot.sound}). isDerivedFrom the Intelligence Bound spine (10.5281/zenodo.19317982). Lean certifies the reasoning, not empirical magnitudes. This version (v1.0.0): mints the S6 series; founding deposit (CTT / DGT / HDT / MELT).
Authors: Hart, Justin, Aristotle (Harmonic)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7166448933