CSRankings counts publication in top conferences to rank professors/universities. But this encourages researchers to pursue quantity rather than quality. We propose impactrank.org/, a new university ranking system that tries to measure quality instead of quantity of publications. How can we measure the quality of the publications? We believe that 1) The quality of research is best understood and evaluated by peers in the same research area; 2) With careful and informed use, LLMs can reveal the implicit quality judgments that peers convey through their citation practices and writing across large volumes of scholarly work. Hence, we developed the new ranking system where we analyze research papers from major AI conferences with LLMs. For each paper, we ask an LLM what are the 5 most important papers to this paper. In other words, the five works that most strongly influence the study. By doing this, we trace which papers and authors are consistently seen as inspirational and foundational to new discoveries in the field. We ran the model on all papers from top conferences in machine learning, computer vision, natural language processing and information retrieval from 2020 - 2025, and filtered references to only have those from 2000 onwards. Next, we map these influential authors to their affiliated universities using the CSRankings name–affiliation database. Each time a paper is recognized as one of the “top five references” in another work, its authors and their institutions receive credit. To keep the scoring fair, points are divided by the number of co-authors, ensuring balanced recognition across collaborations. The result is a new kind of academic ranking: one that rewards universities not just for publishing often, but for producing research that endures, inspires, and drives the field forward. This approach highlights scholarly influence and provides students, researchers, and institutions with a clearer picture of where the most impactful work is happening. Note that we believe that CSRankings had substantially improved university rankings in computer science by replacing subjective, reputation-based measures, such as those in US News, with more objective indicators, but the LLM era allows us to do something potentially better! Due to computational resource limits, we were only able to run it with a small 7B language model. It is also a project primarily led by undergraduate and master students from Oregon State University and University of California Santa Cruz. As a result, the system is very much a work in progress and will inevitably contain errors and blind spots. We actively welcome community feedback, new collaborators and contributions of GPU compute so that we can run larger LLMs, obtain more reliable results and improve the methodology.