Theoretical framework establishes bounds for intelligence to acquire information, suggesting implications for cognitive learning.
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).
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Hart et al. (2026) studied this question.
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