FARS: A Fully Automated Research System Deployed at Scale
Summary
Recent automated research systems show that language-model agents can generate hypotheses, run experiments, and write complete manuscripts, but most evidence still comes from selected examples, human-framed topics, or a few pre-defined research tasks.
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Computer Science > Artificial Intelligence
arXiv:2606.31651v1 (cs)
[Submitted on 30 Jun 2026]
Title:FARS: A Fully Automated Research System Deployed at Scale
Authors:Qiong Tang,Xiangkun Hu,Xiangyang Liu,Yiran Chen,Yunfan Shao
View a PDF of the paper titled FARS: A Fully Automated Research System Deployed at Scale, by Qiong Tang and 4 other authors
Abstract:Recent automated research systems show that language-model agents can generate hypotheses, run experiments, and write complete manuscripts, but most evidence still comes from selected examples, human-framed topics, or a few pre-defined research tasks. We present FARS (Fully Automated Research System), a fully automated AI-for-AI research system designed to operate across research topics at scale. FARS autonomously generates and advances projects through ideation, planning, experimentation, and writing, using stage-specific agents coordinated through a shared workspace that records proposals, code, logs, results, and manuscripts. In its first public deployment, FARS produced 166 complete research papers spanning 67 fine-grained AI/ML topics while preserving intermediate artifacts as an auditable corpus rather than a curated set of successes. We evaluate this corpus with 282 structured reviews from volunteer reviewers covering 140 papers, including overall ratings, sub-scores, integrity checks, and LLM-use disclosure. The reviews indicate that FARS can produce review-worthy and occasionally strong AI/ML research artifacts in a large-scale public deployment, while also exposing recurring failure modes in narrow experimental scope, methodological limitations, and integrity issues.
| | | | --- | --- | | Subjects: | Artificial Intelligence (cs.AI) | | Cite as: |arXiv:2606.31651[cs.AI] | | | (orarXiv:2606.31651v1[cs.AI] for this version) | | |https://doi.org/10.48550/arXiv.2606.31651<br>Focus to learn more<br>arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qiong Tang \[view email]
[v1] Tue, 30 Jun 2026 13:30:24 UTC (3,568 KB)
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