DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal
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.
6978308a026bdf0473116e36
Peixuan Han
claimed_verified
2026-01-27T09:42:34.672Z
638d601b5e14c2f38678fb3a
https://cdn-avatars.huggingface.co/v1/production/uploads/638d601b5e14c2f38678fb3a/Elu-TTd97lGy7YL7eKRuZ.jpeg
韩沛煊
HakHan
user
HakHan
6978308a026bdf0473116e37
Yingjie Yu
6978308a026bdf0473116e38
Jingjun Xu
6978308a026bdf0473116e39
Jiaxuan You
admin_assigned
2026-01-27T14:34:15.055Z
68acb86d5c1a878ddb38210f
/avatars/7553021c5e1f7494752e06b0c8d3d7f5.svg
jiaxuanYou
jiaxuanYou
user
jiaxuanYou
DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal
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.
An agentic framework for automatic academic rebuttal generation that decomposes reviews, retrieves evidence, plans rebuttal strategies, and generates persuasive responses with human-level performance using an 8B model.
large language models academic rebuttal agentic framework long-context understanding automated reasoning multi-perspective analysis explainable AI