Thermodynamic Theory of Algorithmic Catalysis Developed Within Watts-per-Intelligence Framework
Researchers have developed a thermodynamic theory of algorithmic catalysis that identifies reusable computational structures reducing irreversible operations while maintaining specific constraints. The work proves that class-specific speed-ups are upper-bounded by algorithmic mutual information and establishes a minimum thermodynamic cost via Landauer erasure. This framework provides information-theoretic constraints on intelligent computation and helps situate learned systems within fundamental physical limits.
A new theoretical framework extends the watts-per-intelligence model by developing a thermodynamic theory of algorithmic catalysis. The research identifies reusable computational structures that can reduce irreversible operations for specific task classes while satisfying bounded restoration and structural selectivity constraints. The authors 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 according to Landauer's principle of erasure. A coupling theorem derived from these results establishes a lower bound on the deployment horizon required for an algorithmic catalyst to become energetically favorable. The framework is demonstrated on affine SAT problems and provides a way to situate contemporary learned systems within fundamental information-thermodynamic constraints on intelligent computation.
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- arXiv cs.AICenter
Watts-per-Intelligence Part II: Algorithmic Catalysis
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