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Watts-per-Intelligence Part II: Algorithmic Catalysis

2026-04-21 13:36:33
Elija Perrier
arXiv_AI
   
Abstract

We develop a thermodynamic theory of algorithmic catalysis within the watts-per-intelligence framework, identifying reusable computational structures that reduce irreversible operations for a task class while satisfying bounded restoration and structural selectivity constraints. We prove that any class-specific speed-up is upper-bounded by the algorithmic mutual information between the substrate and the class descriptor, and that installing this information incurs a minimum thermodynamic cost via Landauer erasure. Combining these results yields a coupling theorem that lower-bounds the deployment horizon required for a catalyst to be energetically favourable. The framework is illustrated on an affine SAT class and situates contemporary learned systems within a unified information-thermodynamic constraint on intelligent computation.

Abstract (translated)

URL

https://arxiv.org/abs/2604.20897

PDF

https://arxiv.org/pdf/2604.20897.pdf

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