Watts-per-Intelligence Part II: Algorithmic Catalysis
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 encoding 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 an algorithmic catalyst to be energetically favourable. The framework is il...
| Authors | Elija Perrier |
|---|---|
| Primary category | cs.IT |
| Published | 21 April 2026 |
| Last updated at source | 6 June 2026 |
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