提出流变指数，评估模型在动态环境中的自适应能力。
Fluidity Index: Next-Generation Super-intelligence Benchmarks
- 用状态偏差衡量模型在变化环境中的响应精度
- 要求模型具备至少二阶自适应能力以实现持续计算
- 适合评估真实世界中需要连续决策的超智能系统
本文提出流变指数（Fluidity Index, FI），用于量化模型在动态、可扩展环境中的适应能力。该基准通过评估初始、当前及未来环境状态的偏差，考察模型在上下文切换与连续性方面的表现。区分封闭式与开放式评测，优先采用闭环开放式真实场景基准来测试适应性。方法旨在衡量模型理解、预测并调整状态变化的能力。真正具备超智能特性的模型应具备至少二阶自适应能力，能够通过数字补给实现自我维持计算，以达到最优流变性。
原文摘要 · Abstract (English)
This paper introduces the Fluidity Index (FI) to quantify model adaptability in dynamic, scaling environments. The benchmark evaluates response accuracy based on deviations in initial, current, and future environment states, assessing context switching and continuity. We distinguish between closed-ended and open-ended benchmarks, prioritizing closed-loop open-ended real-world benchmarks to test adaptability. The approach measures a model's ability to understand, predict, and adjust to state changes in scaling environments. A truly super-intelligent model should exhibit at least second-order adaptability, enabling self-sustained computation through digital replenishment for optimal fluidity.
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