Fluidity Index: Next-Generation Super-Intelligence Benchmarks (arxiv.org)

A new paper proposes the Fluidity Index (FI), a benchmark designed to quantify how well AI models adapt to dynamic, scaling environments. Rather than measuring static task performance, FI scores response accuracy against deviations across initial, current, and predicted future environment states, explicitly testing context switching, continuity, and the ability to anticipate and adjust to change. The authors separate closed-ended from open-ended evaluations and argue that closed-loop open-ended, real‑world benchmarks are the most revealing for adaptability. Associated code, data and demos are referenced, suggesting reproducible evaluation pipelines. For the AI/ML community, FI reframes robustness testing toward temporal and scale-aware adaptability: models must not only react but predict and self-manage their computational posture. The paper introduces the notion of second-order adaptability — the capacity for self-sustained computation through “digital replenishment” (i.e., maintaining or reconfiguring resources to preserve fluidity) — as a hallmark of super‑intelligent systems. Practically, FI targets continual learning, online adaptation, and resilience under environment drift, pushing benchmark design toward closed-loop, real-world scenarios and raising new evaluation and safety considerations for next-generation, autonomy-capable models.