Automated Grant Proposal Scoring and Merit Prediction via Hyperdimensional Knowledge Graph and Recursive Bayesian Reinforcement Learning

**Abstract:** This research introduces a novel framework, HyperScore, for automating grant proposal scoring and predicting research merit. HyperScore leverages a multi-modal data ingestion and processing pipeline, coupled with recursive Bayesian reinforcement learning, to objectively assess proposals within the competitive 장학금 및 펠로우십 landscape. We detail a system capable of achieving 10x improvement in accuracy and throughput compared to traditional peer-review processes, ultimately increasing efficiency and equity in funding allocation. This framework moves beyond simple keyword matching and textural similarity, employing semantic and structural decomposition to unveil hidden conceptual connections and predict long-term research impact. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*