[2603.22376] AI Co-Scientist for Ranking: Discovering Novel Search Ranking Models alongside LLM-based AI Agents with Cloud Computing Access

Computer Science > Information Retrieval arXiv:2603.22376 (cs) [Submitted on 23 Mar 2026] Title:AI Co-Scientist for Ranking: Discovering Novel Search Ranking Models alongside LLM-based AI Agents with Cloud Computing Access Authors:Liwei Wu, Cho-Jui Hsieh View a PDF of the paper titled AI Co-Scientist for Ranking: Discovering Novel Search Ranking Models alongside LLM-based AI Agents with Cloud Computing Access, by Liwei Wu and 1 other authors View PDF HTML (experimental) Abstract:Recent advances in AI agents for software engineering and scientific discovery have demonstrated remarkable capabilities, yet their application to developing novel ranking models in commercial search engines remains unexplored. In this paper, we present an AI Co-Scientist framework that automates the full search ranking research pipeline: from idea generation to code implementation and GPU training job scheduling with expert in the loop. Our approach strategically employs single-LLM agents for routine tasks while leveraging multi-LLM consensus agents (GPT 5.2, Gemini Pro 3, and Claude Opus 4.5) for challenging phases such as results analysis and idea generation. To our knowledge, this is the first study in the ranking community to utilize an AI Co-Scientist framework for algorithmic research. We demonstrate that this framework discovered a novel technique for handling sequence features, with all model enhancements produced automatically, yielding substantial offline performance improvements. Our fi...