Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
Youran Sun, Xingyu Ren, Chugang Yi, Jiaxuan Guo, Kejia Zhang, Jianda Du, Haizhao Yang
原文摘要
Large language models are making research scalable. We present \textsc{Agon}, a research orchestrator designed to probe the limits of automated research. \textsc{Agon} is built on six design principles: Prompt Economy, Future-Facing, Minimal Prompts, OmniDisciplinary, Massive Parallelism, and Zero-Code. We deployed \textsc{Agon} across domains using only brief topic descriptions as starting points and no human-written code. These deployments demonstrate scalability but also reveal previously unseen classes of failure that are difficult to address through prompt engineering. We organize these failures into a taxonomy along four dimensions: severity, fixability, visibility, and capability locus. Together, these results show that \textsc{Agon} is pushing research towards a new paradigm: machines scale, humans steer. Our code is publicly available at \href{https://github.com/AutoResearch-Factory/Agon/tree/lite}{this GitHub repository}.
cs.SE · cs.AI · cs.CL · cs.MA