Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
Large language models are making research production scalable, shifting the bottleneck from producing artifacts to judging claims. We present \textsc{Agon}, a research orchestrator that validates what can be checked inside the workflow and leaves the remaining judgments to human scientists. \textsc{Agon} is built on six design principles: Prompt Economy, Future-Facing, Minimal Prompts, OmniDisciplinary, Massive Parallelism, and Zero-Code. We ran \textsc{Agon} across domains for 444 iterations of Prompt Economy loops, using only small starting topics and no human-written experimental code. These deployments demonstrate scalability while exposing new classes of failure. We organize these fa...
| Authors | Youran Sun, Xingyu Ren, Chugang Yi et al. |
|---|---|
| Primary category | cs.SE |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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