CPR-IE:A Compression-Prediction-Resource Intelligence Efficiency Metric

Abstract:Comparing intelligent systems under deployment constraints requires more than this http URL paper develops Compression-Prediction-Resource Intelligence Efficiency (CPR-IE) as a protocol-relative ordering by representational economy, predictive quality, and resourceburden. The analysis separates two questions-how raw resource consumption is represented, andhow the resulting attributes are aggregated. Proportional-increment composition uniquely yieldslogarithmic cumulative burden, and context-independent ratio response yields power responsesto compression, prediction, and burden; with reference normalization the representation is I(C,P,T).We prove Pareto consistency, unit invariance, boundary behavior, trade-off identities, ranking-stability regions, and cross-task aggregation. A translog parent model makes interaction restrictions explicit, and further results establish cardinal and ordinal identification, sub-Gaussianfinite-sample ranking guarantees, robust selection under exponent uncertainty, and deterministicregret bounds. Minimum description length, algorithmic complexity, proper scoring rules, varia-tional inference, and Landauer's principle motivate measurement choices but do not entail theformula. CPR-IE is a constructed efficiency representation, not a universal law or a definition ofintelligence itself.

Artificial Intelligence (cs.AI)

arXiv:2609.04809 [cs.AI]

(or arXiv:2609.04809v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.04809 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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