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Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
[Submitted on 23 Jun 2026] · 2026-06-24 · via cs.CL updates on arXiv.org

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Abstract: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 failures into a taxonomy along severity, fixability, visibility, and capability locus. The taxonomy separates failures the loops can see and fix from those that require human judgment. Together, these results show that \textsc{Agon} is pushing research toward a new paradigm: machine scales, human steers.

Submission history

From: Youran Sun [view email]
[v1] Tue, 23 Jun 2026 05:57:09 UTC (368 KB)

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