AI evidence extraction

Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy

Youran Sun, Xingyu Ren, Chugang Yi, Jiaxuan Guo, Kejia Zhang, Jianda Du et al. · arXiv (Cornell University) · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.48550/arxiv.2606.24177

Methodology & findings

Study design

Case study deployment of an autonomous research system across multiple domains, with failure taxonomy analysis and iterative refinement through Prompt Economy loops

Primary method

design science; iterative deployment-based evaluation

Main result

The study demonstrates that "Large language models are making research production scalable, shifting the bottleneck from producing artifacts to judging claims." The system successfully ran "across domains for 444 iterations of Prompt Economy loops, using only small starting topics and no human-written experimental code," while exposing and organizing failures into a taxonomy based on severity, fixability, visibility, and capability locus.

Research paradigm

Design science / Human-machine systems engineering

Author conclusions

The authors conclude that "Agon is built on six design principles: Prompt Economy, Future-Facing, Minimal Prompts, OmniDisciplinary, Massive Parallelism, and Zero-Code," and that "together, these results show that Agon is pushing research toward a new paradigm: machine scales, human steers."

Risk of bias

No human-written experimental code control comparison; No baseline system comparison mentioned; Selection bias potential: only small starting topics used (limited scope representation); No cross-validation by independent evaluators mentioned

Limitations

The paper exposes "new classes of failure" through its deployments and "organize[s] these failures into a taxonomy along severity, fixability, visibility, and capability locus," indicating that "failures the loops can see and fix" are separated from "those that require human judgment." Specific failure details and quantified limitations are not elaborated in the abstract.

Open questions raised

The paper identifies the need for better understanding of failure classes that require human judgment and the mechanisms for human-machine collaboration in research validation.
The paper identifies a need to understand and categorize new failure modes in large-scale autonomous research systems, and to develop methods for determining which judgments require human expertise versus automated validation.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 68%

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