arXiv:2606.05443v1 Announce Type: cross Abstract: The rapid pace of scientific publishing has made the identification and synthesis of high-impact work an increasingly urgent challenge. We introduce MIRAI (Multi-year Inference of Research trends and Academic Impact), a deep learning framework that predicts paper impact using only it's title, abstract, and publication date. We train MIRAI on the arXiv academic graph to predict 5-year PageRank and citation counts, achieving Spearman's $\rho$ of 0.4686 on PageRank prediction and 0.6192 on citation prediction for papers published in 2021. We propo
The proliferation of scientific papers and the urgent need for efficient knowledge synthesis drives the development of AI tools for academic impact prediction.
This development could fundamentally alter how academic research is evaluated, funded, and disseminated, accelerating high-impact discoveries and influencing research trajectories.
The ability to predict research impact before extensive peer review or citation accumulation will shift academic incentive structures and potentially democratize early-stage research identification.
- · Research institutions
- · Funding bodies
- · Emerging researchers
- · AI/ML researchers
- · Traditional academic gatekeepers
- · Low-impact research
Researchers and institutions will strategically optimize paper titles and abstracts for AI impact prediction models.
Funding allocations will increasingly be influenced by projected impact scores, potentially leading to more efficient R&D investments.
The definition of 'impact' itself might evolve, shaped by what these predictive AI models are trained to optimize, potentially leading to novel research directions.
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