利用多工作流LLM管道评估AI生成的研究计划新颖性
Evaluating Novelty in AI-Generated Research Plans Using Multi-Workflow LLM Pipelines
AI总结：
本文通过多工作流LLM管道评估AI生成研究计划的新颖性，发现递归分解和长上下文工作流在新颖性和可行性方面表现更优。
摘要：
将大型语言模型（LLMs）整合到科学生态系统中，引发了关于AI生成研究的创造性和原创性的重要问题。近期的研究指出，在单步提示方法中存在
英文摘要：
The integration of Large Language Models (LLMs) into the scientific ecosystem raises fundamental questions about the creativity and originality of AI-generated research. Recent work has identified ``smart plagiarism'' as a concern in single-step prompting approaches, where models reproduce existing ideas with terminological shifts. This paper investigates whether agentic workflows -- multi-step systems employing iterative reasoning, evolutionary search, and recursive decomposition -- can generate more novel and feasible research plans. We benchmark five reasoning architectures: Reflection-based iterative refinement, Sakana AI v2 evolutionary algorithms, Google Co-Scientist multi-agent framework, GPT Deep Research (GPT-5.1) recursive decomposition, and Gemini~3 Pro multimodal long-context pipeline. Using evaluations from thirty proposals each on novelty, feasibility, and impact, we find that decomposition-based and long-context workflows achieve mean novelty of 4.17/5, while reflection-based approaches score significantly lower (2.33/5). Results reveal varied performance across research domains, with high-performing workflows maintaining feasibility without sacrificing creativity. These findings support the view that carefully designed multi-stage agentic workflows can advance AI-assisted research ideation.
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