mlsanityData to 7 Oct 2026

AI Must Embrace Specialization via Superhuman Adaptable Intelligence

arXiv 2602.23643 ↗First posted 27 Feb 2026cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is an AI that can do everything a human can do, but are humans truly general? In this paper, we address what's wrong with our conception of AGI, and why, even in its most coherent formulation, it is a flawed concept to describe the future of AI. We explore whether the most widely accepted definitions are plausible, useful, and truly general. We argue that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduce Superhuman Adaptable Intelligence (SAI). SAI is defined as intelligence that can learn to exceed humans at anything important that we can do, and that can fill in the skill gaps where humans are incapable. We then lay out how SAI can help hone a discussion around AI that was blurred by an overloaded definition of AGI, and extrapolate the implications of using it as a guide for the future.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 27 Feb 2026.

Builds on

15 of 53 references.

References · 15

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
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  7. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 982
  8. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  9. 2025Absolute Zero: Reinforced Self-play Reasoning with Zero Datacited by 323
  10. 2025Training Agents Inside of Scalable World Modelscited by 122
  11. 2023Levels of AGI for Operationalizing Progress on the Path to AGIcited by 94
  12. 2020Limitations of Autoregressive Models and Their Alternativescited by 54
3 more
  1. 2025Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learningcited by 33
  2. 2025Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithmscited by 12
  3. 2025Do Large Language Models (Really) Need Statistical Foundations?cited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026SPADE: Self-Play in Adaptive Synthetic Executable Environmentscited by 6
  2. 2026CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Modelscited by 4
  3. 2026Mirage Probes: How Vision Models Fake Visual Understandingcited by 4
  4. 2026Evidence of an Emergent "Self" in Continual Robot Learningcited by 0
  5. 2026Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMscited by 0
  6. 2026Understanding Generalization Requires Universal Inductioncited by 0