{"ID":3084689,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-06T20:54:36.964885582Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05443","arxiv_id":"2606.05443","title":"MIRAI: Prediction and Generation of High-Impact Academic Research","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 $ρ$ 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 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.","short_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...","url_abs":"https://arxiv.org/abs/2606.05443","url_pdf":"https://arxiv.org/pdf/2606.05443v1","authors":"[\"Alex Li\",\"Joseph Jacobson\"]","published":"2026-06-03T21:06:01Z","proceeding":"cs.DL","tasks":"[\"cs.DL\",\"cs.CL\"]","methods":"[\"Large Language Model\"]","project_urls":"[\"https://predict-paper-impact.vercel.app\"]","has_code":false}