# MIRAI: Prediction and Generation of High-Impact Academic Research

arXiv: https://arxiv.org/abs/2606.05443 · HTML: https://mlsanity.com/paper/2606.05443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Can large language models provide useful feedback on research papers? A large-scale empirical analysis](https://mlsanity.com/paper/2310.01783/) (2023)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://mlsanity.com/paper/2505.13259/) (2025)
- [LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models](https://mlsanity.com/paper/2307.07889/) (2023)
- [Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks](https://mlsanity.com/paper/2511.07025/) (2025)
- [Hermes 3 Technical Report](https://mlsanity.com/paper/2408.11857/) (2024)
- [Quantifying Long-Term Scientific Impact](https://mlsanity.com/paper/1306.3293/) (2013)
- [The strain on scientific publishing](https://mlsanity.com/paper/2309.15884/) (2023)
- [From Words to Worth: Newborn Article Impact Prediction with LLM](https://mlsanity.com/paper/2408.03934/) (2024)
- [Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network](https://mlsanity.com/paper/2511.02238/) (2025)
- [Predicting citation impact of research papers using GPT and other text embeddings](https://mlsanity.com/paper/2407.19942/) (2024)
- [Is preprint the future of science? A thirty year journey of online preprint services](https://mlsanity.com/paper/2102.09066/) (2021)
- [Identification of milestone papers through time-balanced network centrality](https://mlsanity.com/paper/1608.08414/) (2016)
- [The potential of preprints to accelerate scholarly communication - A bibliometric analysis based on selected journals](https://mlsanity.com/paper/1306.4856/) (2013)
- [Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data](https://mlsanity.com/paper/2001.05414/) (2020)
- [Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network](https://mlsanity.com/paper/2210.06346/) (2022)

## Abstract (arXiv)

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.