DRPG（分解、检索、规划、生成）：一种用于学术反驳的智能体框架

摘要

尽管大型语言模型（LLMs）在科研工作流程中的应用日益广泛，但针对学术反驳——这一学术交流与同行评审关键环节的自动化支持仍鲜有研究。现有方法通常依赖现成的LLMs或简单流程，难以实现长上下文理解，且往往无法生成具有针对性及说服力的回应。本文提出DRPG，一种基于智能体的自动化学术反驳生成框架，其运行包含四个步骤：将评审意见分解为原子化问题、从论文中检索相关证据、规划反驳策略、据此生成回应。值得注意的是，DRPG框架中的规划器在识别最优反驳方向时准确率超过98%。在顶级会议数据上的实验表明，DRPG显著优于现有反驳流程，仅使用80亿参数模型即实现了超越平均人类水平的表现。我们的分析进一步验证了规划器设计的有效性及其在提供多视角可解释建议方面的价值。实验还表明DRPG在更复杂的多轮对话场景中同样表现优异。这些成果凸显了DRPG框架在生成高质量反驳内容、支持学术讨论规模化发展方面的潜力。本工作代码已开源：https://github.com/ulab-uiuc/DRPG-RebuttalAgent。

English

Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and peer review, remains largely underexplored. Existing approaches typically rely on off-the-shelf LLMs or simple pipelines, which struggle with long-context understanding and often fail to produce targeted and persuasive responses. In this paper, we propose DRPG, an agentic framework for automatic academic rebuttal generation that operates through four steps: Decompose reviews into atomic concerns, Retrieve relevant evidence from the paper, Plan rebuttal strategies, and Generate responses accordingly. Notably, the Planner in DRPG reaches over 98% accuracy in identifying the most feasible rebuttal direction. Experiments on data from top-tier conferences demonstrate that DRPG significantly outperforms existing rebuttal pipelines and achieves performance beyond the average human level using only an 8B model. Our analysis further demonstrates the effectiveness of the planner design and its value in providing multi-perspective and explainable suggestions. We also showed that DRPG works well in a more complex multi-round setting. These results highlight the effectiveness of DRPG and its potential to provide high-quality rebuttal content and support the scaling of academic discussions. Codes for this work are available at https://github.com/ulab-uiuc/DRPG-RebuttalAgent.

DRPG（分解、检索、规划、生成）：一种用于学术反驳的智能体框架

DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal

摘要

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