ReviewingAgents: Automated Multi-Agent Reviews

• ReviewingAgents are modular, autonomous software entities harnessing large language models to generate, assess, and refine review workflows.
• They employ architectures like leader/worker hierarchies and criterion-specialized agents to achieve precise, context-aware evaluations.
• Evaluation protocols using metrics such as precision, recall, and semantic F1 demonstrate their enhanced performance in scientific, code, and systematic reviews.

ReviewingAgents are modular, autonomous or semi-autonomous software entities—typically instantiated using LLMs—designed to generate, assess, or facilitate feedback in scholarly, professional, or technical review workflows. This paradigm leverages prompt-engineered roles, multi-agent communication, and specialized evaluation logic to overcome context limitations, improve coverage of diverse review criteria, and emulate the deliberative processes of expert human reviewers. ReviewingAgents have been applied to tasks spanning scientific peer review, code review, protocol audit, systematic literature review (SLR) assessment, and document quality assurance, exhibiting state-of-the-art performance in both accuracy and scalability across domains.

1. Core Architectures and Agent Decomposition

ReviewingAgents frameworks apply agent decomposition to both increase review specificity and overcome the context-length constraints of LLM backends. Representative architectures include:

• Leader/Worker Hierarchies: MARG assigns each agent a contiguous chunk of a paper, with a leader agent centralizing coordination and synthesis. This architecture allows parallel chunk-level processing while retaining unified global task management. Worker agents process assigned sections, obey leader instructions, and prune histories to fit within context budgets (D'arcy et al., 2024).
• Criterion-Specialized Agents: Expert agents are instantiated for distinct aspects of the evaluation, e.g., "clarity," "experimental rigor," or "impact/novelty." These expert agents are not pre-trained with additional knowledge but are prompted to focus on their defined criteria, which enhances detection of nuanced or easily overlooked issues (D'arcy et al., 2024, Zou et al., 12 Jan 2026, Baek et al., 2024). In DIAGPaper, a Customizer module dynamically plans paper-specific dimensions, spawning reviewer agents for each, while an adversarial Author agent validates or rebuts each proposed weakness at a granular level (Zou et al., 12 Jan 2026).
• Message-Passing and Deliberation: Internal agent discussions are orchestrated by explicit message rounds (e.g., "SEND MESSAGE: ..."), with mechanisms to detect messaging loops or protocol violations. This supports persistent multi-step reasoning and dynamic context requests among agents, which is critical for effective review of long or complex documents (D'arcy et al., 2024).
• Task-Oriented Pipelines: Complex workflows, such as meta-review or literature review synthesis, chain distinct agent modules (e.g., summarizing, critiquing, meta-reviewing) or instantiate taskforces (exploration, exploitation, experience) to mitigate compounding errors and provide integrated self-correction (Zhang et al., 6 Aug 2025, Purkayastha et al., 7 Aug 2025).

2. Evaluation Protocols and Metrics

ReviewingAgents address the multi-faceted nature of review assessment by incorporating a suite of formalized evaluation metrics and structured user studies:

• Overlap and Alignment Metrics: Generated review comments are aligned with human-authored references by directional intersection (e.g., $C_\text{gen} ⋂_g C_\text{real}$); precision, recall, and (pseudo-)Jaccard indices are standard for quantifying overlap (D'arcy et al., 2024). Semantic F1 and specificity metrics are applied to evaluate the validity and focus of LLM-generated weaknesses (Zou et al., 12 Jan 2026).
• Scalar and Ordinal Ratings: Human judgments and agent outputs are often mapped to discrete scales for specificity (4-point), accuracy (3-level), and overall helpfulness, with logistic regression confirming that specificity and factual correctness are strong predictors of usefulness (D'arcy et al., 2024).
• Iterative Improvement: In iterative systems such as ResearchAgent, per-criterion scores are aggregated (e.g., arithmetic mean across five criteria) to produce sub-idea and full-idea quantitative ratings. Iterative prompt-refinement consistently raises mean scores until convergence (Baek et al., 2024).
• End-to-End System Benchmarks: Large-scale experiments—e.g., ReviewBench for paper reviewing, AAAR and ReviewCritique for weakness detection, and custom datasets for meta-reviewing—compare ReviewingAgent architectures with leading LLM baselines, measuring diversity, semantic consistency, and acceptance alignment (Gao et al., 11 Mar 2025, Zou et al., 12 Jan 2026, Purkayastha et al., 7 Aug 2025).

3. Impact on Scientific and Code Review Quality

ReviewingAgents have achieved significant improvements over conventional or single-agent LLM approaches across multiple review settings:

• Scientific Paper Feedback: MARG reduced the rate of generic comments from 60% (LiZCa/SARG-B) to 29% and more than doubled the number of "good" comments per paper as rated by expert users (3.7 vs. 1.7) (D'arcy et al., 2024). DIAGPaper's multi-agent debate and prioritization produced more valid, severe, and paper-specific weaknesses than single-agent or baseline multi-agent systems, with nearly 89% human-validated correctness for novel, previously unannotated critiques (Zou et al., 12 Jan 2026).
• Research Ideation: Human-aligned ReviewingAgents in ResearchAgent guided iterative idea generation, with mean sub-idea quality improving measurably over two to three rounds (as quantified by human and LLM-evaluator agreement) (Baek et al., 2024).
• Meta-Reviewing: Dialogue agents trained on self-refined synthetic conversations outperformed zero-shot LLMs, halving error rates in meta-review composition and reducing overall expert effort by ≈43%, without compromising decision quality (Purkayastha et al., 7 Aug 2025).
• SLR and Protocol Audit: Specialized ReviewingAgents achieved 84% raw agreement and 0.78 Cohen’s κ with expert-annotated PRISMA checklists, demonstrating robust generalization across medical, education, ecology, and economics domains (Mushtaq et al., 21 Sep 2025).
• Code Review: In code contexts, agentic reviews with expert decomposition (e.g., CodeAgent, RevAgent) surpassed monolithic LLMs in vulnerability detection (e.g., 92.96% confirmation rate in VA vs. <52% for GPT-4), category-aware comment quality (e.g., +12.9% BLEU-4 for RevAgent), and efficiency (RevAgent 4× faster than previous multi-agent baselines) (Tang et al., 2024, Li et al., 1 Nov 2025).

4. Governing and Assessing Autonomous Agent Contributions

With the shift to AI-authored code and agent-mediated research output, ReviewingAgents serve not only as generators and evaluators of content but also as enablers of scalable governance:

• Theme Taxonomies and Pre-Review Triage: Topic-modeling pipelines augmented by LLM clustering can classify dominant review themes in agentic pull requests with 78–79% Top-1 accuracy against human annotation, supporting risk-targeted human oversight (Haider et al., 27 Jan 2026). Persistent gaps in testing and security are leading predictors of rejection and are efficiently flagged by such automated annotation protocols.
• Effort Prediction and Circuit Breakers: Early-stage, feature-based classifiers (e.g., LightGBM models using static structural diff features) achieve AUC ≈0.96 in predicting high-review-effort PRs, enabling maintainers to divert 69% of total review burden to dedicated "deep review" pipelines and minimize wasted human time on churn-prone, abandoned agent contributions (Minh et al., 2 Jan 2026).
• Agent-as-a-Judge: To assess agentic systems at scale, modular ReviewingAgents operate as meta-evaluators—hierarchically decomposing requirement checking, file localization, trace analysis, and binary compliance scoring. In benchmarking (DevAI: 55 tasks, 365 requirements), Agent-as-a-Judge aligns with human judgments up to 92.1%, dramatically outperforming single-pass LLM-based judges and reducing human labor by >97% (Zhuge et al., 2024).

5. Theoretical Guarantees and Mechanism Design

Many ReviewingAgents frameworks adopt explicit mechanism design to ensure robustness, fairness, and incentive compatibility:

• Endogenous Matching and Rating Dynamics: Autonomously maintained reviewer ratings, coupled with dynamic, rating-dependent matching, provably resolve both adverse selection and moral hazard. Peer reviewers are incentivized to exert high effort by the prospect of being matched to higher-quality submissions in future rounds, resulting in stable, nonzero review quality and social welfare maxima unattainable under one-shot or exogenous matching (Xiao et al., 2014).
• Agent Specialization and Adversarial Validation: Modular workflows that include adversarial author rebuttals (DIAGPaper) prune invalid or over-strict critiques, while prioritizer modules reweight retained issues by empirically estimated impact (from conference meta-review data) to surface only the most consequential weaknesses (Zou et al., 12 Jan 2026).

6. Limitations and Future Directions

Current ReviewingAgents architectures face several constraints:

• Inference Cost and Latency: Systems such as MARG-S incur 10× higher token throughput per paper, limiting real-time applicability without further optimization (D'arcy et al., 2024). Multi-agent pipelines with extensive debate or self-correction loops (DIAGPaper, MATC) may exhibit significant runtime overhead (Zou et al., 12 Jan 2026, Zhang et al., 6 Aug 2025).
• Context Limitations and Data Coverage: LLM agents remain sensitive to context window truncation, missing non-textual content (e.g., equations, figures), or underperforming on domain-shifted or extra-long documents (D'arcy et al., 2024, Gao et al., 11 Mar 2025).
• Imperfect Pruning and Partial Validity: Even after adversarial rebuttal, a significant fraction of invalid or marginally relevant comments persist, requiring improved debate protocols, more nuanced validity estimation, or the integration of external retrieval for fact-checking (D'arcy et al., 2024, Zou et al., 12 Jan 2026).
• Robustness and Generalization: Current systems are mostly benchmarked on AI/ML conference papers, NLP code, or selected SLRs; domain extension may demand new criteria or prompting strategies (Gao et al., 11 Mar 2025, Zou et al., 12 Jan 2026, Mushtaq et al., 21 Sep 2025).

Future research priorities include dynamic agent routing to control inference cost, retrieval-augmented validation for improved factual accuracy, uncertainty quantification, fine-grained and continuous scoring functions, and large-scale A/B deployment to measure end-user impact and acceptability.

7. Comparative Table of Key Framework Features

Framework	Domain	Agent Decomposition	Validation Protocol	Major Gains Reported	Noted Limitations
MARG (D'arcy et al., 2024)	Scientific papers	Leader/workers/experts	Human ratings, overlap metrics	3.7 vs. 1.7 good comments/paper; -31% generic rate	High computational cost, imperfect pruning
DIAGPaper (Zou et al., 12 Jan 2026)	Paper weaknesses	Criterion-based reviewers, Author rebuttal, Prioritizer	Validity/adversarial rebuttal, human benchmarks	F1 51.9 (vs. 47.4), Specificity↑, ~40–60% invalid pruned	Runtime, over-strictness, domain scope
ReviewAgents (Gao et al., 11 Mar 2025)	Paper reviewing	Reviewer/multi-agent + meta-review	Human alignment, ReviewBench, ablation	Closer match to human review, consistent semantic/sentiment	Truncation, domain focus
AgentReview (Jin et al., 2024)	Peer review dynamics	Reviewer/Author/Area-Chair	Simulation, sociological modeling	Quantifies ~37% decision variation from bias	Not for live peer review
CodeAgent (Tang et al., 2024), RevAgent (Li et al., 1 Nov 2025)	Code review	Specialized agents + QA or critic	Task-specific F1, recall, human readability	+10–13% BLEU/ROUGE, strong bug category detection	Code size/token limits, domain coverage
ResearchAgent (Baek et al., 2024)	Research ideation	Feedback loop via separate criteria-agents	Iterative rating, human calibrated	5–10% mean score improvement, aligns with human prefs	Rubric granularity, LLM quality dependence
Agent-as-a-Judge (Zhuge et al., 2024)	Agent eval, code dev	Modular agents for requirement/graph analysis	Requirement-level alignment, cost analysis	92% human alignment, 97% time/cost savings	Focused on code, needs broader test

In conclusion, ReviewingAgents constitute a modular, criterion-aligned, and increasingly robust paradigm for automating and enhancing review quality, offering both empirical and theoretical advances for scientific, technical, and enterprise review ecosystems.