提出新指标衡量智能系统在资源约束下的综合效率。
CPR-IE:A Compression-Prediction-Resource Intelligence Efficiency Metric
- 基于压缩、预测与资源消耗的三重优化构建评估框架
- 实现对系统效率的稳定排序，支持跨任务比较
- 适合评估部署受限场景下的模型选择与优化
在部署约束下评估智能系统需超越预测准确率。本文提出压缩-预测-资源智能效率（CPR-IE）指标，通过表示经济性、预测质量与资源负担的协议相关排序实现综合评估。分析分离了资源消耗表征方式与属性聚合机制：比例增量组合产生对数累积负担，上下文无关比值响应使压缩、预测与负担呈现幂律响应；参考归一化后表征为 I(C,P,T)。证明了帕累托一致性、单位不变性、边界行为、权衡恒等式、排序稳定性区域及跨任务聚合性。变对数父模型显式揭示交互限制，进一步建立基数与序数识别、亚高斯有限样本排序保证、指数不确定性下的鲁棒选择及确定性后悔界。最小描述长度、算法复杂性、合理评分规则、变分推断与兰道尔原理启发测量选择，但不决定公式本身。CPR-IE 是构造的效率表征，非普适定律或智能定义。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。