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Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, Zhipeng Zhang
TL;DR
Rebuttal assistance must align reviewer concerns with verifiable manuscript evidence, but existing approaches can hallucinate, omit critiques, or obscure their reasoning. RebuttalAgent addresses this with concern decomposition, hybrid evidence construction, and inspectable planning before drafting. On RebuttalBench, it improves reliable rebuttal-assistance requirements while preserving author control; the framework remains limited by latency, artifact reuse, and venue variation.
Problem
Existing rebuttal-assistance approaches can hallucinate or over-commit to unverified claims, while interactive workflows conceal intermediate reasoning and depend on extensive prompting.
Method
RebuttalAgent uses a multi-agent verify-then-write workflow that atomizes concerns, constructs internal and external evidence, and verifies evidence-linked response plans before drafting.
Results
RebuttalAgent improves coverage, traceability, coherence, and argumentation quality over prior direct-to-text and chat-LLM baselines on the proposed benchmark.
Takeaways & Limitations
The framework provides structured, verifiable rebuttal assistance while keeping authors responsible for strategic decisions and final wording.
Takeaways & Limitations
The system prioritizes transparency over minimal latency, does not fully reuse intermediate artifacts, and does not yet encode venue- or subfield-specific conventions.
Abstract
from arXiv · showhide
Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce $\textbf{RebuttalAgent}$, the first multi-agents framework that reframes rebuttal generation as an evidence-centric planning task. Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts by synthesizing compressed summaries with high-fidelity text while integrating an autonomous and on-demand external search module to resolve concerns requiring outside literature. By generating an inspectable response plan before drafting, $\textbf{RebuttalAgent}$ ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed $\textbf{RebuttalBench}$ and demonstrate that our pipeline outperforms strong baselines in coverage, faithfulness, and strategic coherence, offering a transparent and controllable assistant for the peer review process.
1 Introduction
Rebuttal writing requires careful interpretation of reviewer intent and verifiable manuscript grounding under tight deadlines. RebuttalAgent addresses this by organizing concerns and evidence, verifying response plans before drafting, and improving coverage, traceability, and coherence.
- Motivation: Rebuttal writing requires synthesizing reviewer intent with verifiable manuscript details under strict turnaround windows.The task extends beyond textual composition because authors must provide evidence-backed clarifications and actionable revisions.
- Limitations of Existing Approaches: Direct-to-text models can hallucinate experimental results or over-commit to unverified claims because they learn response patterns rather than strategic reasoning.Interactive chat workflows also require lengthy prompting while concealing concern parsing and evidence retrieval.
- RebuttalAgent: RebuttalAgent reframes assistance as a decision-and-evidence organization problem governed by coverage, faithfulness, grounding, and global-consistency constraints.Its verify-then-write workflow is designed to make the reasoning process inspectable.
- RebuttalAgent: The system atomizes reviews, constructs dual-source evidence bundles, and audits response logic before drafting through human-in-the-loop checkpoints.These artifacts expose the basis for claims and help prevent contradictory commitments across reviewer responses.
- Evaluation: RebuttalAgent outperforms direct-to-text baselines and chat-LLMs across coverage, evidence traceability, global coherence, and argumentation quality.The evaluation prioritizes practical usability and reliability rather than text fluency alone.
- Implications: The framework significantly reduces rebuttal-writing cognitive burden while leaving authors responsible for their scientific defense.Its structured assistance is intended to preserve author control over final decisions and wording.
2 Related Works
Related work motivates agentic systems for reliable, tool-supported tasks and extends that direction to peer-review communication. Existing direct-generation methods overlook explicit concern decomposition and evidence-based response planning.
- LLM Agents: Purely parametric generation can accumulate errors when tasks require multi-step planning, fresh evidence, and interaction with external systems.This motivates agents that plan and act within dynamic, goal-directed frameworks.
- LLM Agents: Multi-agent frameworks support role specialization and structured collaboration, while benchmarks evaluate tool use and end-to-end task success.These developments motivate applying agentic systems to evidence retrieval and iterative rebuttal refinement.
- AI-Assisted Peer Review: Existing direct-generation approaches overlook explicit decomposition of reviewer concerns and planning of evidence-based responses.This gap distinguishes rebuttal assistance from ordinary text-generation evaluation.
3 RebuttalAgent
RebuttalAgent converts rebuttal writing into an inspectable workflow that structures concerns, retrieves targeted evidence, and verifies response plans before drafting. Specialized agents and checkers expose decisions while preserving author responsibility.
- Workflow: Specialized agents and lightweight checkers decompose reasoning into structured artifacts before producing a formal rebuttal letter.Authors can refine the plan through a human-in-the-loop mechanism.
- Input Structuring: The pipeline distills manuscripts and reviews into compact representations that improve retrieval precision under context limitations.High-fidelity passages remain selectively accessible for fine-grained evidence retrieval.
- Evidence Construction: Targeted evidence bundles anchor each reviewer concern to specific, verifiable facts before planning and drafting.The system searches compressed manuscript representations and selectively expands relevant sections into hybrid contexts.
- Planning and Drafting: For concerns requiring new experiments or baselines, the system inhibits result generation and produces concrete Action Items for author verification.This structural pause prevents fabricated outcomes and supports author refinement of the proposed actions.
4 RebuttalBench
RebuttalBench evaluates rebuttals from real peer-review interactions using multidimensional criteria that emphasize concern coverage, evidence grounding, and communication quality beyond surface fluency.
- Benchmark Design: The benchmark targets technical density and argumentative complexity rather than simple text-to-text matching.Its protocol measures atomic-concern identification and grounding of counter-arguments in verifiable facts.
- Dataset: RebuttalBench is derived from real-world OpenReview interactions and pairs reviewer critiques with corresponding author rebuttals.Its challenge split selects 20 papers with more than 100 reviewers and diverse positive and negative follow-up signals.
- Evaluation Rubric: RebuttalBench scores Relevance, Argumentation Quality, and Communication Quality with a fine-grained 0-5 LLM-as-judge rubric.The three dimensions contain nine components and are averaged within each dimension.
- Evaluation Rubric: Communication Quality assesses respectful tone, clear structure, and unambiguous language that support productive reviewer-author discussion.The metric complements relevance and argumentation measures rather than evaluating fluency alone.
- Evaluation Rubric: The evaluator also produces structured diagnoses of strengths, weaknesses, and suggested improvements for qualitative analysis.Detailed scoring anchors and implementation are provided separately.
5 Experiments
Experiments evaluate RebuttalAgent against matched LLM baselines and through module ablations, showing broad gains from structured evidence-centered processing and complementary intermediate artifacts.
- Experimental Setup: RebuttalAgent is evaluated in fully automated experiments against strong closed-source LLMs, with matched backbones and controlled decoding settings.The evaluation uses the same base models for fair comparisons and keeps the evaluation protocol fixed across ablations.
- Main Results: RebuttalAgent consistently improves evaluation dimensions over matched LLM baselines, with gains reaching +0.78 in coverage, +1.33 in specificity, and +0.63 in rebuttal quality.The largest gains occur in Relevance and Argumentation Quality, while Communication Quality improvements are smaller but consistent.
- Main Results: The weakest backbone, GPT5-mini, gains about +0.55 on average across nine components, compared with +0.33 for Gemini-3-Flash.On relevance sub-scores, GPT5-mini improves by roughly +0.89 versus about +0.47 for Gemini-3-Flash.
- Main Results: Under Gemini-3-Flash, RebuttalAgent improves Relevance, Argumentation Quality, and Communication Quality across their reported component scores.Coverage rises from 4.00 to 4.51, rebuttal quality from 3.56 to 4.07, and statement clarity from 4.08 to 4.28.
- Ablation Study: Removing Evidence Construction causes the largest degradation, reducing Coverage from 4.51 to 4.26 and constructiveness from 4.09 to 3.82.The ablation disables external literature retrieval and citation-ready evidence briefs while retaining complete rebuttal drafts.
- Ablation Study: Removing checkers slightly increases Coverage from 4.51 to 4.54 but lowers Suggestion Constructiveness from 4.09 to 4.05, reflecting verbose over-generation.The analysis attributes the coverage increase to broader, less focused responses filled with generic explanations.
- Qualitative Analysis: RebuttalAgent exposes response strategies, evidence-linked clarification points, action items, and scoped deliverables instead of generating unsupported results.These intermediate artifacts allow authors to validate or edit the plan before drafting and retain control over final commitments.
6 Conclusion
The paper presents RebuttalAgent as a multi-agent framework that creates structured, evidence-linked artifacts before drafting. On RebuttalBench, it improves reliable rebuttal-assistance requirements through a transparent verify-then-write workflow that keeps authors in control.
- Conclusion: RebuttalAgent constructs structured, evidence-linked intermediate artifacts before drafting rebuttal text.The framework decomposes the process into concern structuring, query-conditioned context building, external evidence synthesis, and response planning.
- Conclusion: The framework improves traceability and cross-point coherence while keeping authors responsible for strategic decisions and final wording.Its author-centric benchmark evaluates relevance, global coherence, and argumentation quality beyond text fluency.
- Conclusion: Benchmark experiments support a transparent verify-then-write workflow that reduces cognitive burden while preserving author control over final wording.The conclusion highlights reliable rebuttal assistance as the supported outcome.
Limitations and Future Work
The framework prioritizes reliability and inspectability but identifies latency, artifact reuse, and cross-venue alignment as remaining limitations and future-work directions.
- Limitations: The system prioritizes transparency and inspectability over minimal latency.Caching artifacts and adaptive early-exit policies are proposed to reduce cost and runtime.
- Limitations: Intermediate artifacts are not yet fully reused across concerns or author iterations.More aggressive caching and incremental updates could reduce redundant computation during revisions or recurring concerns.
- Future Work: Rebuttal conventions vary across venues and subfields, limiting alignment without additional style and policy constraints.Venue-specific formatting and tone constraints are proposed as a lightweight extension.
Broader Impact and Ethics Statement
The paper frames rebuttal assistance as author-supervised support with inspectable safeguards, while acknowledging risks of misleading outputs and privacy concerns.
- Ethical Risks: Automated rebuttal assistance may produce misleading responses or expose unpublished manuscripts and reviews.Identified risks include exaggerated claims, hallucinated results, unrealistic commitments, and privacy risks.
- Mitigations: Authors remain responsible for the final stance, commitments, and wording because the system is an assistance tool rather than an autonomous rebuttal system.The design preserves human responsibility for final communication.
- Mitigations: Inspectability and explicit checks support responsible use by exposing concerns, evidence links, and response plans before drafting.Checks address coverage, faithfulness to the manuscript, evidence traceability, and global coherence.
- Ethical Commitment: The paper explicitly acknowledges ethical issues and incorporates safeguards to promote responsible use.
A Evaluation Dataset
The evaluation benchmark is derived from the ICLR 2023 subset of RE2 and constructed through outcome classification, reliability stratification, curation, and baseline generation.
- Data Source: Approximately 9,310 RE2 entries from ICLR 2023 provide the source corpus for benchmark construction.The corpus is processed through a four-stage pipeline.
- Outcome Classification: Entries are classified as Improved or Unimproved according to changes in review scores or acceptance status.
- Reliability Stratification: Three reliability tiers stratify cases using evidence objectivity and LLM confidence.Tier 1 is Gold Standard, Tier 2 is High Confidence, and Tier 3 is Medium Confidence.
- Ground Truth Curation: A balanced test set of 20 representative papers is curated with diverse positive and negative review samples across tiers.The selection prioritizes papers with high review volumes.
- Baseline Protocol: Baselines generate rebuttals over multiple dialogue rounds using paper text, current reviews, optional prior-round abstracts, and fixed prompts.Each round produces a factual abstract under 200 words for the next round, while outputs and token usage are logged.
B Evaluation Metric
The evaluation scores rebuttals with a fine-grained, rubric-based framework covering relevance, argumentation, and communication, using component-level LLM judgments and aggregation.
- Scoring Protocol: A 0-5 scale with halfpoint increments captures nuanced response-quality differences beyond binary judgments.
- Dimensions and Weights: The final score combines R-Score, A-Score, and C-Score, each decomposed into three components.The nine components are coverage, semantic alignment, specificity, logic consistency, evidence support, response engagement, professional tone, clarity, and constructiveness.
- Relevance: R-Score measures whether and how well responses address reviewer concerns through coverage, semantic alignment, and specificity.Specificity includes precise references such as equations or table rows rather than generic statements.
- Argumentation: A-Score evaluates logically sound, substantively supported arguments using logic consistency, evidence support, and response engagement.
- Communication: Communication quality assesses professional tone, clarity, and constructiveness, including actionable steps rather than vague commitments.
- Aggregation: An LLM judge assigns each component a 0-5 score with a justification and overall diagnosis, while dimension scores average their three components.The main paper reports overall weighted scores alongside per-dimension and per-component breakdowns.
- Ambiguous Cases: The benchmark retains a small fraction of ambiguous cases to represent noisy peer review and stress-test evaluation on contradictory signals.Ambiguous cases can contain mixed emotions or contradictory rhetoric, such as praise alongside persistent doubts about novelty.
D.3 Human Annotation Study
The study validates automated evaluation against human judgments and across evaluator models, finding that RebuttalAgent consistently outperforms direct-to-text baselines.
- 100.0% accuracy separates Resolved from Unresolved cases against expert annotators, with Precision, Recall, and F1 all at 1.000.
- 97.7% of human-annotated Tier 1 cases are recalled, while leakage from Tier 1 into noisy Tier 3 cases is 0.0%.
- The validation targets alignment with human experts and robustness across evaluator models for the automated LLM-as-judge rubric.
- RebuttalAgent consistently outperforms direct-to-text baselines across evaluation dimensions, with the overall average rising from 3.84 to 4.13 under the GPT judge.
- Automated scores closely align with human evaluations, and RebuttalAgent outperforms baselines under both evaluation paradigms.
E.3 Limitations of Standard N-gram Metrics
Standard BLEU and ROUGE scores can reward superficial text overlap while penalizing scientifically substantive rebuttal reasoning, motivating a specialized evaluation rubric.
- BLEU and ROUGE are omitted from primary evaluation because they do not assess semantic accuracy or logical argumentation quality in academic defenses.
- A direct-to-text baseline receives BLEU-4 = 6.96 and ROUGE-L = 18.49 through conservative synonym replacement.
- RebuttalAgent constructs a deeper defense using Information Bottleneck theory, dimensionality analysis, and Mutual Information arguments.
- RebuttalAgent receives lower overlap scores—BLEU-4 = 2.38 and ROUGE-L = 14.05—despite providing more substantive logical elaboration.
F Additional Baseline Comparisons
Additional comparisons show RebuttalAgent outperforming standard RAG and generic multi-agent baselines, while checker-based verification improves precision and actionable utility despite slightly lower automated coverage.
- Additional baseline comparisons: RebuttalAgent achieves an overall average score of 4.23, exceeding the expanded baseline methods on RebuttalBench.
- Additional baseline comparisons: Jiu Jitsu scores 1.42 and standard RAG scores 3.11, with RAG particularly weak in Specificity at 2.07 and Evidence Support at 2.57.
- Checker ablation: Removing the Checker marginally increases automated Coverage but degrades precision and actionable utility, creating the reported “Coverage Paradox”.
- Checker ablation: Without the Checker, generation becomes verbose and unconstrained, inflating coverage through keywords while adding irrelevant or hallucinated concepts.
- Checker ablation: The Checker enforces evidence linkage, removes tangential concepts, and prioritizes actionable commitments over verbose explanations.
- Checker ablation: The revised theoretical response distinguishes micro-level Reward from macro-level Utility and conditions Eq. 4 on opponent action history τ_t−1.
Methodological triangulation v.s. Linear request fulfillment
The paper responds to concerns about the topographic metric by narrowing its evidential role and adding complementary tests of compositionality. The proposed revision combines real-world validation, symbol decodability, latent-space linearity, and clearer reporting of geometric versus functional similarity.
- Reframing the metric: The Hausdorff-based topographic metric is reframed as a geometric visualization rather than definitive proof of compositionality.The authors note that distance-based metrics on raw signals may be agnostic to functional meaning.
- Decodability analysis: Systematic decodability tests use k-NN to classify composite symbols from constituent representations learned from single-feature utterances.The planned analysis reports constituent-identification accuracy.
- Latent-space analysis: Latent-space linearity is assessed by measuring reconstruction error for composite embeddings represented as linear combinations of constituent embeddings.The revision proposes calculating MSE for z(r_ij) ≈ w_i z(r_i) + w_j z(r_j).
- Integrated revision: The revision distinguishes geometric similarity from functional compositionality and supplements the original analysis with quantitative evidence from decodability and latent-space tests.The authors characterize these additions as feasible using existing models, metric code, and lightweight inference scripts.
- External validation: Real-world validation applies the topographic score ρ to curated ideograms, traffic signs, emojis, and other symbols with intuitive compositionality judgments.The proposed examples include a restaurant symbol composed of fork and knife features.