发表机构
College of Information Engineering, Shanghai Maritime University（上海海事大学信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CPR-IE度量，通过比例增量组合等方法，为部署约束下智能系统的比较提供了具有帕累托一致性等性质的效率表征，明确了其非普遍定律的定位。
AI 中文摘要
在部署约束下比较智能系统需要的不仅是现有指标，本文提出压缩-预测-资源智能效率（CPR-IE），作为一种基于表征经济性、预测质量和资源负担的协议相对排序方法。分析区分了两个问题：原始资源消耗如何表征，以及所得属性如何聚合。比例增量组合唯一产生对数累积负担，上下文无关比率响应产生对压缩、预测和负担的幂响应；结合参考归一化，该表征为I(C,P,T)。我们证明了帕累托一致性、单位不变性、边界行为、权衡恒等式、排名稳定区域和跨任务聚合。超越对数父模型明确了交互限制，进一步结果确立了基数和序数识别、亚高斯有限样本排名保证、指数不确定性下的稳健选择以及确定性遗憾边界。最小描述长度、算法复杂度、适当计分规则、变分推理和兰道尔原理为测量选择提供了动机，但并不导出该公式。CPR-IE是一种构建的效率表征，而非普遍定律或智能本身的定义。
英文摘要
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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