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arXiv · 2606.05443

MIRAI: Prediction and Generation of High-Impact Academic Research

Alex Li·Joseph Jacobson
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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 propose a research ideation pipeline built on top of MIRAI that produces research ideas oriented towards high impact. These ideas were judged as more impactful than a baseline without MIRAI by an unbiased LLM judge at a 4:3 ratio. We make the 5-year citation prediction model publicly available at https://predict-paper-impact.vercel.app.

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Alex Li, Joseph Jacobson. 2026-06-03. MIRAI: Prediction and Generation of High-Impact Academic Research. https://arxiv.org/abs/2606.05443

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