# Artificial Adaptive Intelligence: The Missing Stage Between Narrow and General Intelligence

arXiv: https://arxiv.org/abs/2605.16844 · HTML: https://mlsanity.com/paper/2605.16844/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 106 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 20 of 20)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [An Empirical Model of Large-Batch Training](https://mlsanity.com/paper/1812.06162/) (2018)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [A Simple Convergence Proof of Adam and Adagrad](https://mlsanity.com/paper/2003.02395/) (2020)
- [AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence](https://mlsanity.com/paper/1905.10985/) (2019)
- [Learning to Continually Learn](https://mlsanity.com/paper/2002.09571/) (2020)
- [Advancing Eurasia Fire Understanding Through Machine Learning Techniques](https://mlsanity.com/paper/2502.17023/) (2025)
- [MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing](https://mlsanity.com/paper/2511.13234/) (2025)
- [PSTNet: Physically-Structured Turbulence Network](https://mlsanity.com/paper/2603.07957/) (2026)
- [ORCA -- Online Regime Correlation Analyzer](https://mlsanity.com/paper/2604.17251/) (2026)
- [OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN](https://mlsanity.com/paper/2007.05205/) (2020)
- [AlphaJet: Automated Conceptual Aircraft Synthesis via Disentangled Generative Priors and Topology-Preserving Evolutionary Search](https://mlsanity.com/paper/2604.26337/) (2026)

## Built on by (most cited first, 2 of 2)

- [Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling](https://mlsanity.com/paper/2606.13695/) (2026)
- [The Cost of a Physics Prior Is Bounded by the Ablation Gap](https://mlsanity.com/paper/2608.21059/) (2026)

## Abstract (arXiv)

Between the narrow systems we deploy and the general intelligence we speculate about lies an entire regime of machine behavior that has never received its own name. This monograph argues that this regime is not empty: it is where meta-learning, neural architecture search, AutoML, continual learning, evolutionary computation, and physics-informed modeling have quietly converged on a common principle, namely the steady removal of the human from the loop of parameter specification. We name this regime Artificial Adaptive Intelligence (AAI) and define it operationally: a system exhibits AAI to the extent that it requires no human-specified tunable hyperparameters while maintaining competitive performance across a diverse distribution of tasks. To make the definition quantitative, we introduce an adaptivity index that measures progress along an axis orthogonal to scale, combining the fraction of hyperparameters absorbed by the system with the performance ratio against a task-specialized baseline. We develop the principle of parametric minimality and ground it in the minimum description length framework, showing that the appropriate hyperparameter count is data-determined rather than designer-determined. We then organize the field around three pathways to minimality: data- and task-aware configuration, structural and evolutionary morphing, and in-training self-adaptation. We analyze their stability, convergence, and governance implications, and illustrate them through case studies spanning aerospace design, financial regime detection, turbulence modeling, ecological dynamics, and vision-language systems. The thesis is that the path from ANI to AGI passes through AAI, and that naming this stage changes what we measure, what we build, and what we call a success.