A recent study has unveiled a novel framework designed to precisely quantify the divergence between research ideas generated by large language models (LLMs) and those originating from human scholars. Diverging from previous evaluation methods that typically judge AI-produced concepts on individual merits such as novelty, feasibility, or expert preference, this research focuses on directly measuring the *distance* of LLM ideation from established human benchmarks1. The methodology involves constructing an extensive evaluation system derived from a curated collection of high-quality human research papers. For each paper, the framework systematically reverse-engineers the underlying ideation process, establishing a robust reference against which to characterize how far contemporary LLM-generated ideas are from human intellectual output. This approach offers a more profound insight into AI's capabilities for contributing to foundational research, moving beyond subjective qualitative assessments. Practitioners need to understand this quantified gap to accurately assess the strategic utility and potential limitations of integrating LLMs into advanced research and development initiatives.