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

• URL: http://arxiv.org/abs/2609.04809v1
• Date: Fri, 04 Sep 2026 07:06:21 GMT
• Title: CPR-IE:A Compression-Prediction-Resource Intelligence Efficiency Metric
• Abstract summary: We develop a protocol-relative ordering by representational economy, predictive quality, and resourceburden.<n>CPR-IE is a constructed efficiency representation, not a universal law or a definition ofintelligence itself.
• Score: 0.685316573653194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comparing intelligent systems under deployment constraints requires more than predictiveaccuracy.This 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.

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