arXiv subjects

Yiling Lin

Publications and source records attributed to Yiling Lin.

12 recordsLinked to original sources

arXiv2026

The Longevity of Innovation

Modern science is organized around specialization in training and teamwork. Scientists develop deep expertise within a field and combine complementary knowledge through collaboration to solve complex problems. Yet whether specialization is the most effective path to sustained innovation remains unclear. Here we introduce a quantitative framework that distinguishes generalists from specialists based on scaling patterns of disciplinary mobility while remaining independent of career age and productivity. Applying this framework to 49 million publications produced by 3 million scientists between 1900 and 2020, we examine how research style relates to innovation, learning, collaboration, and productivity. We find that scientists who move across fields are more likely to sustain innovative contributions throughout their careers, whereas those who remain within narrow fields exhibit the age-related decline in innovation. Generalists are less anchored to the literature of their training. They are more likely to pursue research independently, and, when they collaborate, they preferentially partner with other generalists. Teams with a greater share of generalists produce more innovative research, even after accounting for differences in knowledge diversity. Despite these advantages, generalists publish fewer papers on average and have become less common over time. These findings reveal a tension between the longevity of scientific careers and the longevity of scientific innovation.

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arXiv2025

Innovation by Displacement

New ideas are often thought to arise from recombining existing knowledge. Yet despite rapid publication growth - and expanding opportunities for recombination - scientific breakthroughs remain rare. This gap between productivity and progress challenges recombinant growth theory as the prevailing account of innovation. We argue that the limitation of this theory lies in treating ideas solely as complements, overlooking that breakthroughs often arise when ideas act as substitutes. To test this, we integrate scientist interviews, bibliometric validation, and machine learning analysis of 41 million papers (1965 - 2024). Interviews reveal that breakthroughs are marked not by novelty (Atypicality) alone but by their ability to displace dominant ideas (Disruption). Large-scale analysis confirms that novelty and disruption represent distinct innovation mechanisms: they are negatively correlated across domains, periods, team sizes, and paper versions. Novel papers extend dominant ideas across topics and attract immediate attention; disruptive papers displace them within the same topic and generate lasting influence. Hence, progress slows not from lack of effort but because most research extends rather than overturns ideas. Applying this perspective reveals distinct roles of theories and methods in scientific change: methods more often drive breakthroughs, whereas theories tend to be novel but rarely disruptive, reinforcing the dominance of established ideas.

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arXiv2025

Can Recombination Displace Dominant Scientific Ideas

The combination of diverse, pre-existing knowledge is a common explanation for scientific breakthroughs. However, a paradox exists: while scientific output and the potential for such recombination have grown exponentially, the rate of breakthrough discoveries has not. To explore this paradox, our study examines 41 million scientific articles from 1965 to 2024. We measure two key properties for each paper: atypicality, which quantifies the combination of knowledge from conceptually distant areas, and disruption. We demonstrate that these metrics capture distinct processes. Atypicality is characteristic of work that extends established concepts into new topical areas (a form of cross-topic recombination). Disruption, in contrast, signifies the replacement of a dominant idea within its own topic.

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arXiv2025

The Disruption Index Measures Displacement Between a Paper and Its Most Cited Reference

Initially developed to capture technical innovation and later adapted to identify scientific breakthroughs, the Disruption Index (D-index) offers the first quantitative framework for analyzing transformative research. Despite its promise, prior studies have struggled to clarify its theoretical foundations, raising concerns about potential bias. Here, we show that-contrary to the common belief that the D-index measures absolute innovation-it captures relative innovation: a paper's ability to displace its most-cited reference. In this way, the D-index reflects scientific progress as the replacement of older answers with newer ones to the same fundamental question-much like light bulbs replacing candles. We support this insight through mathematical analysis, expert surveys, and large-scale bibliometric evidence. To facilitate replication, validation, and broader use, we release a dataset of D-index values for 49 million journal articles (1800-2024) based on OpenAlex.

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arXiv2025

Is Science Inevitable?

Using large-scale citation data and a breakthrough metric, the study systematically evaluates the inevitability of scientific breakthroughs. We find that scientific breakthroughs emerge as multiple discoveries rather than singular events. Through analysis of over 40 million journal articles, we identify multiple discoveries as papers that independently displace the same reference using the Disruption Index (D-index), suggesting functional equivalence. Our findings support Merton's core argument that scientific discoveries arise from historical context rather than individual genius. The results reveal a long-tail distribution pattern of multiple discoveries across various datasets, challenging Merton's Poisson model while reinforcing the structural inevitability of scientific progress.

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arXiv2025

Team Size and Its Negative Impact on the Disruption Index

As science transitions from the age of lone geniuses to an era of collaborative teams, the question of whether large teams can sustain the creativity of individuals and continue driving innovation has become increasingly important. Our previous research first revealed a negative relationship between team size and the Disruption Index-a network-based metric of innovation-by analyzing 65 million projects across papers, patents, and software over half a century. This work has sparked lively debates within the scientific community about the robustness of the Disruption Index in capturing the impact of team size on innovation. Here, we present additional evidence that the negative link between team size and disruption holds, even when accounting for factors such as reference length, citation impact, and historical time. We further show how a narrow 5-year window for measuring disruption can misrepresent this relationship as positive, underestimating the long-term disruptive potential of small teams. Like "sleeping beauties," small teams need a decade or more to see their transformative contributions to science.

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arXiv2024

Displacing Science

Recent research on the decline in the paper disruption index (D-index) has sparked heated debates among scholars and garnered significant attention from policymakers and research institution leaders globally. To bridge the gap between policymakers' interest and scholars' skepticism about the D-index, we present this article summarizing key insights from our eight-year investigation, including interviews with scientists across nine disciplines and an analysis of 41 million papers over six decades. Our work confirms the decline in disruptive papers, addresses relevant technical concerns, and makes several original contributions: we clarify that the D-index measures how new ideas render old ones obsolete, suggesting 'Displacing' as an alternative interpretation for 'D'; we show that federal funding agencies like the NIH and NSF are less likely to support disruptive research; and we introduce the 'principle of functional equivalence' to explain the origins of recombining and displacing mechanisms in science, stressing that not all innovation problems are combinatorial, and challenging the belief that AI is the mature solution to scientific innovation. This article aims to promote broader and more accurate use of the D-index in research evaluation and to inspire new funding mechanisms for scientific breakthroughs.

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arXiv2023

Small Teams Propel Fresh Ideas in Science and Technology

The past half-century has seen a dramatic increase in the scale and complexity of scientific research, to which researchers have responded by dedicating more time to education and training, narrowing their areas of specialization, and collaborating in larger teams. A widely held view is that such collaborations, by fostering specialization and encouraging novel combinations of ideas, accelerate scientific innovation. However, recent research challenges this notion, suggesting that small teams and solo researchers consistently disrupt science and technology with fresh ideas and opportunities, while larger teams tend to refine existing ones (Wu et al. 2019). This study, along with other relevant research, has garnered attention for challenging the zeitgeist of our time that views collaboration as the inevitable path forward in scientific and technological advancement. Yet, few studies have re-evaluated its central finding: the innovative advantage of small teams over large ones, using alternative measures. We explore innovation by identifying papers proposing new scientific concepts and patents introducing new technology codes. We analyzed 88 million research articles spanning from 1800 to 2020 and 7 million patent applications from 1976 to 2020 worldwide. Our findings confirm that while large teams contribute to development, small teams play a critical role in innovation by propelling fresh, original ideas in science and technology.

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arXiv2023

The Death of Renaissance Scientist

Scholars are often categorized into two types: hedgehogs (specialists), who focus on working within a specific research field, and foxes (generalists), who actively contribute to a variety of fields. Despite the familiar anecdotes and popularity of this distinction, its empirical foundation has remained largely unexamined. We examine whether the research style of being a fox or a hedgehog is a stable personal trait or an evolving strategy over a scientist's career. Analyzing 2.3 million scholars' publication records over a century, we find that research styles exhibit remarkable stability. Notably, the proportion of fox-like scientists has dramatically declined in the past century, a phenomenon we term "the death of Renaissance scientists." This decline is particularly significant as science shifts toward team collaboration. Teams of foxes consistently outperform teams of hedgehogs in generating new ideas and directions, as confirmed by two emerging innovation metrics for papers: atypicality and disruption. Our research is the first to quantify the process and consequences of the decline of Renaissance scientists. By doing so, we establish a universal link between research styles, demographic shifts, and innovative output.

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arXiv2022

Remote Collaboration Fuses Fewer Breakthrough Ideas

Theories of innovation emphasize the role of social networks and teams as facilitators of breakthrough discoveries. Around the world, scientists and inventors today are more plentiful and interconnected than ever before. But while there are more people making discoveries, and more ideas that can be reconfigured in novel ways, research suggests that new ideas are getting harder to find-contradicting recombinant growth theory. In this paper, we shed new light on this apparent puzzle. Analyzing 20 million research articles and 4 million patent applications across the globe over the past half-century, we begin by documenting the rise of remote collaboration across cities, underlining the growing interconnectedness of scientists and inventors globally. We further show that across all fields, periods, and team sizes, researchers in these remote teams are consistently less likely to make breakthrough discoveries relative to their onsite counterparts. Creating a dataset that allows us to explore the division of labor in knowledge production within teams and across space, we find that among distributed team members, collaboration centers on late-stage, technical tasks involving more codified knowledge. Yet they are less likely to join forces in conceptual tasks-such as conceiving new ideas and designing research-when knowledge is tacit. We conclude that despite striking improvements in digital technology in recent years, remote teams are less likely to integrate the knowledge of their members to produce new, disruptive ideas.

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arXiv2022

Aging and the Narrowing of Scientific Innovation

With rising life expectancies around the world and an older scientific workforce than ever before, what does aging mean for individual scientists, and what do aging scientists mean for scientific progress as a whole? Here we examine how scientists and scholars age in terms of how their ideas and contributions relate to the evolving frontier of knowledge and how demographically aging fields relate to field-level advance. At the individual level, we examine how research experiences and choices can moderate the effects of intellectual aging. At the collective level, we explore mechanisms that link individual and collective aging. Prior research focuses on star scientists, their changing dates and rates of breakthrough success throughout history. We explore this for scientists in all fields over time, drawing upon novel deep learning measurements that allow us not only to trace positive attention through citation but also negative attention through explicit criticism with a novel, comprehensive database of over 20,000 human-validated critical citations. We find that younger scientists tend toward disruptive contributions that push the frontier, while older scientists engage in combinatorial innovation with an aging collection of components. This includes analyzing the impact of the 1994 U.S. Supreme Court ruling on mandatory retirement and examining how unexpected collaborations affect citation patterns.

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arXiv2021

New Directions in Science Emerge from Disconnection and Discord

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.

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