# Cognitive Dark Matter: Measuring What AI Misses

arXiv: https://arxiv.org/abs/2603.03414 · HTML: https://mlsanity.com/paper/2603.03414/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2026.

## Built on by (most cited first, 5 of 5)

- [Implicit Rule Induction with Test-Time Task Embeddings in ARC-like Tasks](https://mlsanity.com/paper/2609.21181/) (2026)
- [Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners](https://mlsanity.com/paper/2605.08019/) (2026)
- [PotARCin: Multi-Dimensional Evaluation of Skill Acquisition in Abstract Reasoning Tasks](https://mlsanity.com/paper/2609.27288/) (2026)
- [How Much is Brain Data Worth for Machine Learning?](https://mlsanity.com/paper/2605.09243/) (2026)
- [Using profiles of cognitive capability to assess AI suitability for workplace tasks](https://mlsanity.com/paper/2608.25623/) (2026)

## Abstract (arXiv)

We propose that the jagged intelligence landscape of modern AI systems arises from a missing training signal that we call ``cognitive dark matter'' (CDM): brain functions that meaningfully shape behavior yet are hard to infer from behavior alone. We identify key CDM domains---metacognition, cognitive flexibility, lifelong learning, abductive reasoning, social and common-sense reasoning, and emotional intelligence---and present evidence CDM-loaded functions are largely unmeasured in current AI benchmarks, and that large-scale neuroscience training datasets which could be used to instill these capabilities do not yet exist. We then outline a research program centered on three complementary data types designed to surface CDM for model training: (i) latent variables from large-scale cognitive models, (ii) process-tracing data such as eye-tracking and think-aloud protocols, and (iii) paired neural--behavioral data. These data will enable AI training on cognitive process rather than behavioral outcome alone, producing models with more general, less jagged intelligence. As a dual benefit, the same data will advance our understanding of human intelligence itself.