# New Directions in Science Emerge from Disconnection and Discord

arXiv: https://arxiv.org/abs/2103.03398 · HTML: https://mlsanity.com/paper/2103.03398/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2021.

## Built on by (most cited first, 8 of 8)

- [Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation](https://mlsanity.com/paper/2402.12590/) (2024)
- [A review on the novelty measurements of academic papers](https://mlsanity.com/paper/2501.17456/) (2025)
- [HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights](https://mlsanity.com/paper/2505.04846/) (2025)
- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)
- [Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations](https://mlsanity.com/paper/2503.18865/) (2025)
- [GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes](https://mlsanity.com/paper/2205.01331/) (2022)
- [Density, asymmetry and citation dynamics in scientific literature](https://mlsanity.com/paper/2506.23366/) (2025)
- [Discovery by Dreaming: Cross-Domain Recombination in Artificial Memory](https://mlsanity.com/paper/2607.16256/) (2026)

## Abstract (arXiv)

Science is built on the scholarly consensus that shifts with time. This raises the question of how new and revolutionary ideas are evaluated and become accepted into the canon of science. Using two recently proposed metrics, we identify papers with high atypicality, which models how research draws upon novel combinations of prior research, and evaluate disruption, which captures the degree to which a study creates a new direction by eclipsing its intellectual forebears. Atypical papers are nearly two times more likely to disrupt science than conventional papers, but this is a slow process taking ten years or longer for disruption scores to converge. We provide the first computational model reformulating atypicality as the distance across latent knowledge spaces learned by neural networks. The evolution of this knowledge space characterizes how yesterday's novelty forms today's scientific conventions, which condition the novelty--and surprise--of tomorrow's breakthroughs.