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

The paper introduces CPR‑IE, a metric that orders intelligent systems by representational economy, predictive quality, and resource burden. It formalizes how raw resource consumption is represented and aggregated, showing that proportional‑increment composition yields logarithmic cumulative burden and context‑independent ratio responses produce power relationships among compression, prediction, and burden. The authors prove properties such as Pareto consistency, unit invariance, and ranking stability, and provide a translog parent model to make interaction restrictions explicit, along with guarantees on ranking and regret bounds.

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