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Fluidity Index: Next-Generation Super-intelligence Benchmarks

arXiv 2510.20636 ↗First posted 23 Oct 2025cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 23 Oct 2025.

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References · 12

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  3. 2022Emergent Abilities of Large Language Modelscited by 2,512
  4. 2020Mastering Atari with Discrete World Modelscited by 1,007
  5. 2024A Survey on Efficient Inference for Large Language Modelscited by 205
  6. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 192
  7. 2023From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inferencecited by 170
  8. 2017Imagination-Augmented Agents for Deep Reinforcement Learningcited by 140
  9. 2025SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoningcited by 48
  10. 2023Evaluation of Test-Time Adaptation Under Computational Time Constraintscited by 15
  11. 2025Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culturecited by 11
  12. 2025Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worldscited by 4

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