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arxiv: 2601.18081 · v2 · submitted 2026-01-26 · 💻 cs.LG

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

Peixuan Han , Yingjie Yu , Jingjun Xu , Jiaxuan You This is my paper

Pith reviewed 2026-05-16 11:12 UTC · model grok-4.3

classification 💻 cs.LG
keywords academic rebuttalagentic frameworkLLM rebuttal generationpeer review automationdecompose retrieve plan generatereview response
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0 comments X

The pith

DRPG generates academic rebuttals that surpass average human performance using only an 8B model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents DRPG as a four-step agentic system that turns reviewer comments into targeted rebuttals by first breaking them into atomic concerns, pulling supporting evidence from the paper, selecting a rebuttal strategy, and then writing the response. Experiments on data from top-tier conferences show this pipeline beats prior automated methods and exceeds the quality of typical human rebuttals when run on an 8B model. The planner module alone reaches over 98 percent accuracy in picking the most workable direction, and the approach also handles multi-round exchanges. If correct, the result suggests that structured decomposition and planning can make high-quality rebuttal writing accessible without large models or extensive human effort.

Core claim

DRPG decomposes reviews into atomic concerns, retrieves relevant evidence from the paper, plans rebuttal strategies with over 98 percent accuracy in identifying feasible directions, and generates responses that outperform existing rebuttal pipelines while reaching performance beyond the average human level on top-tier conference data using only an 8B model.

What carries the argument

The DRPG pipeline, especially its Planner that selects the most feasible rebuttal direction with 98 percent accuracy.

If this is right

  • High-quality rebuttals become feasible with modest-sized open models rather than frontier-scale ones.
  • The same decomposition-plus-planning structure extends to multi-round rebuttal exchanges without major redesign.
  • The planner's explicit strategy choices supply multi-perspective and explainable guidance that authors can review or adapt.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Authors could spend less time on defensive writing and more on core research if the framework is integrated into submission platforms.
  • The approach might transfer to other long-context academic tasks such as response-to-reviewer letters in journal revisions.
  • If planner accuracy remains high on diverse domains, the method could serve as a template for agentic systems in scientific communication beyond rebuttals.

Load-bearing premise

That the evaluation metrics and human comparisons on the collected conference data fairly represent real-world rebuttal quality and that the planner's accuracy holds across different review styles, paper domains, and model backbones.

What would settle it

A new blind rating study on papers from additional conferences where expert reviewers score DRPG outputs against human-written rebuttals and find DRPG scores no higher or statistically lower.

Figures

Figures reproduced from arXiv: 2601.18081 by Jiaxuan You, Jingjun Xu, Peixuan Han, Yingjie Yu.

Figure 1
Figure 1. Figure 1: Overview of DRPG. Bali, 2024; Li et al., 2024; White et al., 2021; Lee et al., 2018) or reinforcement learning (Zhang et al., 2025b; Chen et al., 2025) when ground-truth can be obtained at scale. Following this line of work, we train a Planner to select rebuttal perspectives when designing DRPG. 3 Method This section presents an overview of DRPG, an end￾to-end framework designed to generate coherent and pr… view at source ↗
Figure 2
Figure 2. Figure 2: An example to illustrate how the planner [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance of different rebuttal agents in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: GRPO training configuration for the judge [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: System Prompt for the Decomposer You are an experienced researcher in computer science. You have received a review on a research paper. Your task is to propose up to 5 perspectives to address this point in the rebuttal. - The perspective should either show the reviewer’s point wrong, or show that the work is valuable even though the review is correct. Specifically, You MUST consider the following two types… view at source ↗
Figure 6
Figure 6. Figure 6: System Prompt for the Perspective Generator [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: System Prompt for the Executor for a Whole Review [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: System Prompt for the Executor for Individual Review Points [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: System Prompt for the Rebuttal Judge 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: System Prompt for Comparing Two Rebuttals [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: System Prompt to Predict the Initial Review Score [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: An example of Jiu-Jitsu procedure to generate rebuttal perspective for a review point. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Illustration of the webpage used for human annotation. [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: A case study comparing Decomp amd DRPG. Review Point: The dataset contains only 20 images. DRG Response: We acknowledge the reviewer’s concern regarding the size of the dataset used in our experiments. However, we would like to clarify that the dataset size was intentionally kept small to demonstrate the feasibility and effectiveness of the proposed SinGAN approach in hiding images in deep probabilistic m… view at source ↗
Figure 15
Figure 15. Figure 15: A case study comparing DRG amd DRPG. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes DRPG, an agentic framework for automatic academic rebuttal generation consisting of four steps: Decompose reviews into atomic concerns, Retrieve relevant evidence from the paper, Plan rebuttal strategies, and Generate responses. It claims the Planner component achieves over 98% accuracy in identifying feasible rebuttal directions, that the full system significantly outperforms existing rebuttal pipelines, and that it exceeds average human performance on data from top-tier conferences using only an 8B model. The work also reports effectiveness in multi-round settings and the value of multi-perspective, explainable suggestions, with code released publicly.

Significance. If the empirical results hold under rigorous evaluation, the work would offer a structured, multi-step alternative to direct LLM prompting for rebuttal generation, potentially improving handling of long-context reviews and providing interpretable planning. The reported success with a compact 8B model and open-sourced code would strengthen its practical utility for scaling peer-review support. The decomposition into atomic concerns and planning step represent a clear methodological contribution over prior pipelines.

major comments (2)
  1. [Abstract and Experiments section] The central claim that DRPG 'achieves performance beyond the average human level' (Abstract) rests on an unspecified human baseline. No details are given on the number or source of human rebuttals collected, participant population (e.g., PhD students, senior authors), blinding of raters to model vs. human origin, evaluation rubric, or inter-rater reliability. This omission makes it impossible to assess whether the comparison fairly measures rebuttal quality or is vulnerable to selection bias.
  2. [Experiments section] The Experiments section provides no description of the test set (number of papers/reviews, domain distribution, or overlap with any training data), baseline methods, automatic metrics for rebuttal quality, or statistical tests supporting the outperformance and 98% planner accuracy claims. Without these, the quantitative results cannot be verified or reproduced.
minor comments (1)
  1. [Abstract] The abstract states that 'DRPG works well in a more complex multi-round setting' but does not indicate where the corresponding experiments or analysis appear in the manuscript.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the current manuscript lacks sufficient detail on the human baseline and experimental setup, and we will revise accordingly to improve clarity and reproducibility.

read point-by-point responses

  1. Referee: [Abstract and Experiments section] The central claim that DRPG 'achieves performance beyond the average human level' (Abstract) rests on an unspecified human baseline. No details are given on the number or source of human rebuttals collected, participant population (e.g., PhD students, senior authors), blinding of raters to model vs. human origin, evaluation rubric, or inter-rater reliability. This omission makes it impossible to assess whether the comparison fairly measures rebuttal quality or is vulnerable to selection bias.

    Authors: We agree that the human baseline description is insufficient in the current version. We will revise the Experiments section to add a dedicated subsection specifying the number and source of human rebuttals, participant population and recruitment, blinding procedures for raters, the evaluation rubric, and inter-rater reliability metrics. This will allow readers to assess the fairness of the comparison. revision: yes

  2. Referee: [Experiments section] The Experiments section provides no description of the test set (number of papers/reviews, domain distribution, or overlap with any training data), baseline methods, automatic metrics for rebuttal quality, or statistical tests supporting the outperformance and 98% planner accuracy claims. Without these, the quantitative results cannot be verified or reproduced.

    Authors: We agree that the Experiments section requires explicit descriptions of the test set, baselines, metrics, and statistical tests. We will revise to include the size and domain distribution of the test set, confirmation of no training data overlap, the list of baseline methods, the automatic metrics used, and the statistical tests supporting all performance claims including planner accuracy. revision: yes

Circularity Check

0 steps flagged

No significant circularity in DRPG empirical framework

full rationale

The paper presents DRPG as a four-step agentic pipeline (Decompose, Retrieve, Plan, Generate) for rebuttal generation and supports its claims solely through reported empirical results on top-tier conference data, including planner accuracy >98% and outperformance versus baselines and human-level performance with an 8B model. No equations, self-definitional constructions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The derivation chain consists of independent experimental comparisons without reduction to inputs by construction, satisfying the default expectation of non-circularity for empirical framework papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical performance of an LLM-based agent pipeline. The main unverified premise is the general reliability of current LLMs for following the structured DRPG instructions across domains.

axioms (1)
  • domain assumption Current LLMs can reliably perform decomposition, retrieval, planning, and generation when given structured prompts and access to paper content.
    The entire framework depends on this assumed capability of the underlying 8B model.

pith-pipeline@v0.9.0 · 5550 in / 1242 out tokens · 52106 ms · 2026-05-16T11:12:06.886550+00:00 · methodology

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Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AI for Auto-Research: Roadmap & User Guide

    cs.AI 2026-05 unverdicted novelty 4.0

    The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages · cited by 1 Pith paper · 4 internal anchors

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    The paper introduced two modules, but lacks ablation study which includes only one of them

    with the prompt in Figure 11 to predict initial scores from the rebuttal text and the final scores. Summary statistics of the rebuttal scores are shown in Table 7b. The resulting score distribu- tions meet our expectations, and we further vali- dated the predictions through human analysis of several randomly sampled examples. B.2 Planner Training As descr...

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  13. [13]

    Make sure all responses are factual, respectful, and persuasive

    Be polite, concise, and professional. Make sure all responses are factual, respectful, and persuasive

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  14. [14]

    Question: ...Response:

    Address each comment point-by-point. It’s recommended to format the main part of the rebuttal as: "Question: ...Response: ...". For each point:

    work page

  15. [15]

    - If the comment has misunderstood the paper or missed some content, clarify the point

    For each point, you should respond with clear reasoning, and evidence from the original paper, and your professional knowledge. - If the comment has misunderstood the paper or missed some content, clarify the point. If not, defend your choices and explain why this comment doesn’t undermine your paper. - DO NOT propose suggestions or promises for future re...

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  16. [16]

    Try your best to explain and validate your work, and rebut the concerns raised by the reviewer

    Be confident with your paper. Try your best to explain and validate your work, and rebut the concerns raised by the reviewer

    work page

  17. [17]

    You should directly generate a passage without additional comments or thoughts

    Your rebuttal should be concise and no more than 1000 words. You should directly generate a passage without additional comments or thoughts. Figure 7: System Prompt for the Executor for a Whole Review 14 You are an experienced researcher in computer science. You have written a conference paper in the field of computer science or AI and received a review. ...

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  18. [18]

    Make sure your response is factual, respectful, and persuasive

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  19. [19]

    - If the comment has misunderstood the paper or missed some content, clarify the point

    You should respond with clear reasoning, and evidence from the original paper, and your professional knowledge. - If the comment has misunderstood the paper or missed some content, clarify the point. If not, defend your choices and explain why this comment doesn’t undermine your paper. - DO NOT propose suggestions or promises for future revision or future work

    work page

  20. [20]

    Try your best to explain and validate you work, and rebute the concerns raised by the reviewer

    Be confident with your paper. Try your best to explain and validate you work, and rebute the concerns raised by the reviewer

    work page

  21. [21]

    My final score is X

    Your rebuttal should be concise and no more than 200 words. You should directly generate a paragraph without additional comments or thoughts. Figure 8: System Prompt for the Executor for Individual Review Points You are an experienced academic paper reviewer. You will receive a response from the authors addressing your review comments. Your task is to eva...

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  22. [22]

    **Output only the JSON object** — no extra commentary, no code fences outside the JSON, no explanations

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  23. [23]

    ‘initial_score‘ must be an integer between 1 and 10

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  24. [24]

    ‘opinion‘ must mention 2–4 clear signals or events from the discussion and explain how they affect the score estimate

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  25. [25]

    Avoid hallucination

    Do not invent facts outside the provided discussion text. Avoid hallucination

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  26. [26]

    The experimental setup is unclear. Please specify the hyperparameters and training details

    If the discussion is ambiguous or contradictory, state that in ‘opinion‘ and then give the most likely integer prediction. Usually, the reviewer is confident with their review, which means they only raise or decrease scores where there is sufficient evidence. User: Discussion text: {discussion_text } Final score: {final_score } / 10 Figure 11: System Prom...

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