Watts-per-Intelligence Part II: Algorithmic Catalysis
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917430285041664 |
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| author | Perrier, Elija |
| author_facet | Perrier, Elija |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20897 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Watts-per-Intelligence Part II: Algorithmic Catalysis Perrier, Elija Information Theory Artificial Intelligence Computational Physics 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. |
| title | Watts-per-Intelligence Part II: Algorithmic Catalysis |
| topic | Information Theory Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2604.20897 |