MIRAI: Prediction and Generation of High-Impact Academic Research
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 $ρ$ 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 impac...
| Authors | Alex Li, Joseph Jacobson |
|---|---|
| Primary category | cs.DL |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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