FARS：全自动科研系统大规模部署，产出166篇论文

Skip to main content

arXiv is now an independent nonprofit!Learn more×

Search arXiv

Press Enter to search ·Advanced search

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

View PDFHTML (experimental)

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)

Full-text links:

Access Paper:

View a PDF of the paper titled FARS: A Fully Automated Research System Deployed at Scale, by Qiong Tang and 4 other authors

• View PDF
• HTML (experimental)
• TeX Source

[view license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/ "Rights to this article")

Current browse context:

cs.AI

[< prev](https://arxiv.org/prevnext?id=2606.31651&function=prev&context=cs.AI "previous in cs.AI (accesskey p)")  \|  [next >](https://arxiv.org/prevnext?id=2606.31651&function=next&context=cs.AI "next in cs.AI (accesskey n)")

new\|recent\|2026-06

Change to browse by:

cs

References & Citations

• NASA ADS
• Google Scholar
• Semantic Scholar

export BibTeX citation

Bookmark


Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer _(What is the Explorer?)_

Connected Papers Toggle

Connected Papers _(What is Connected Papers?)_

Litmaps Toggle

Litmaps _(What is Litmaps?)_

scite.ai Toggle

scite Smart Citations _(What are Smart Citations?)_

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv _(What is alphaXiv?)_

Links to Code Toggle

CatalyzeX Code Finder for Papers _(What is CatalyzeX?)_

DagsHub Toggle

DagsHub _(What is DagsHub?)_

GotitPub Toggle

Gotit.pub _(What is GotitPub?)_

Huggingface Toggle

Hugging Face _(What is Huggingface?)_

ScienceCast Toggle

ScienceCast _(What is ScienceCast?)_

Demos

Demos

Replicate Toggle

Replicate _(What is Replicate?)_

Spaces Toggle

Hugging Face Spaces _(What is Spaces?)_

Spaces Toggle

TXYZ.AI _(What is TXYZ.AI?)_

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower _(What are Influence Flowers?)_

Core recommender toggle

CORE Recommender _(What is CORE?)_

• Author
• Venue
• Institution
• Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.

Which authors of this paper are endorsers?\| Disable MathJax (What is MathJax?)