More of the same? Are scientific papers losing originality?

2026-09-26

AI Summary

This study addresses whether the originality of scientific papers has declined amid the exponential growth of academic literature. Leveraging 26,000 papers from the CORE database, this work employs semantic language models and large-scale text comparison techniques to pioneer a method that directly measures textual uniqueness, thereby controlling for confounding effects of literature expansion. Specifically, it quantifies paragraph-level similarity between target papers and recent publications while analyzing longitudinal trends. The findings reveal that by 2025, the proportion of paragraphs exhibiting high similarity to recent works has tripled compared to pre-2021 levels, and this phenomenon is not attributable to citation practices. This research provides empirical evidence of a significant decline in the originality of scientific writing along measurable dimensions, establishing a novel paradigm for evaluating academic innovation.

Abstract

Scientific output is growing rapidly, but it is unclear whether the expanding literature remains original or is increasingly repeating itself. Originality has many dimensions. One dimension can now be measured directly: how textually distinct a paper is from the work that came before it. Using semantic language models, we screened more than 26,000 full-text papers across eight fields in the CORE database (2015--2025) for passages resembling recent literature. We compared every paper against a fixed-size sample of earlier work, so increases in resemblance are not a by-product of literature growth. We find that the share of a paper's text resembling recent work was stable before 2021, then rose sharply, more than tripling by 2025. The increase came not from a few papers borrowing more heavily but from resemblance spreading across the literature: more papers now contain passages that echo recent work, and almost none of this growth is explained by cited work. We also found an analogous increase in passages expressing the same scientific idea. The pattern held across robustness checks and replicated in a second, independent database. Along this one measurable axis of originality, scientific writing has become less distinct from the work preceding it.

Problem

• scientific originality
• textual similarity
• semantic resemblance
• academic literature
• repetitiveness

Innovation

• Semantic Language Models
• Originality Measurement
• Textual Similarity
• Scientific Literature Analysis