Fluidity Index: Next-Generation Super-intelligence Benchmarks
This addresses the need for better benchmarks to test super-intelligent models in real-world, scaling scenarios, though it appears incremental as it builds on existing adaptability concepts.
The paper tackles the problem of quantifying model adaptability in dynamic environments by introducing the Fluidity Index (FI) benchmark, which evaluates response accuracy based on deviations in environment states to assess context switching and continuity.
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.