AI自动生成166篇论文，覆盖67个细分领域，全程可审计。
FARS: A Fully Automated Research System Deployed at Scale
- 用多阶段智能体协作，从构思到写作全流程自动化
- 公开部署产出166篇完整论文，282份评审显示质量达标
- 适合关注AI研究自动化与可信性评估的研究者
近期自动化研究系统表明语言模型代理可生成假说、开展实验并撰写完整论文，但多数证据来自精选案例、人为设定主题或少数预定义任务。我们提出FARS（Fully Automated Research System），一个可在大规模范围内跨研究主题运行的全自动化AI for AI研究系统。FARS通过共享工作区协调各阶段智能体，自主完成选题、规划、实验与写作，记录提案、代码、日志、结果与论文等全过程产物。首次公开部署中，FARS生成了166篇完整研究论文，涵盖67个细粒度的AI/ML主题，并保留中间成果作为可审计语料库而非筛选的成功案例。我们通过282份志愿者评审对其中140篇论文进行结构化评估，包括整体评分、子项评分、完整性检查及LLM使用披露。评审结果显示，FARS在大规模公开部署中能生成具有评审价值甚至优秀的AI/ML研究成果，同时暴露了实验范围过窄、方法局限和诚信问题等常见缺陷。
原文摘要 · Abstract (English)
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.
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