# Realist and Pluralist Conceptions of Intelligence and Their Implications on AI Research

arXiv: https://arxiv.org/abs/2511.15282 · HTML: https://mlsanity.com/paper/2511.15282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 76 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 25 of 25)

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- [Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games](https://mlsanity.com/paper/2402.13399/) (2024)
- [Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms](https://mlsanity.com/paper/2108.13446/) (2021)
- [Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind](https://mlsanity.com/paper/2408.12022/) (2024)
- [Building Effective Safety Guardrails in AI Education Tools](https://mlsanity.com/paper/2508.05360/) (2025)

## Abstract (arXiv)

In this paper, we argue that current AI research operates on a spectrum between two different underlying conceptions of intelligence: Intelligence Realism, which holds that intelligence represents a single, universal capacity measurable across all systems, and Intelligence Pluralism, which views intelligence as diverse, context-dependent capacities that cannot be reduced to a single universal measure. Through an analysis of current debates in AI research, we demonstrate how the conceptions remain largely implicit yet fundamentally shape how empirical evidence gets interpreted across a wide range of areas. These underlying views generate fundamentally different research approaches across three areas. Methodologically, they produce different approaches to model selection, benchmark design, and experimental validation. Interpretively, they lead to contradictory readings of the same empirical phenomena, from capability emergence to system limitations. Regarding AI risk, they generate categorically different assessments: realists view superintelligence as the primary risk and search for unified alignment solutions, while pluralists see diverse threats across different domains requiring context-specific solutions. We argue that making explicit these underlying assumptions can contribute to a clearer understanding of disagreements in AI research.