Consensus Analysis in
Academic Peer Review
Using Search-Augmented Large Language Models to automatically extract critiques, detect disagreements, and generate comprehensive meta-reviews.
Powerful Features for Peer Review Analysis
Our comprehensive pipeline handles every aspect of peer review consensus analysis with state-of-the-art AI models.
Critique Extraction
Automatically extract and categorize critique points from peer reviews into Methodology, Experiments, Clarity, Significance, and Novelty.
Disagreement Detection
Identify conflicts and disagreements between reviewers with quantified disagreement scores from 0 to 1.
Search-Augmented Verification
Retrieve supporting or contradicting evidence from Semantic Scholar, arXiv, Google Scholar, and Tavily.
Evidence-Based Resolution
Resolve reviewer disagreements using AI reasoning powered by DeepSeek-R1 with retrieved evidence.
Meta-Review Generation
Generate comprehensive meta-reviews that synthesize all analyses with actionable recommendations.
Fast & Efficient
Process multiple reviews simultaneously with optimized API calls and intelligent rate limiting.
The Analysis Pipeline
A systematic five-stage workflow that transforms peer reviews into actionable consensus insights.
Critique Extraction
Extract structured critique points from peer reviews and categorize them into five key aspects.
Disagreement Detection
Compare all review pairs to identify conflicts with quantified disagreement scores.
Search & Retrieval
Search academic sources to find evidence supporting or contradicting the critiques.
Disagreement Resolution
Use advanced reasoning to validate critiques and resolve disagreements based on evidence.
Meta-Review Generation
Generate a comprehensive meta-review synthesizing all analyses with recommendations.
How It Works
Get started with MetaSearch in three simple steps.
Input Your Data
Provide the paper title, abstract, and peer reviews in JSON format. You can use our example or enter your own data.
AI Processing
Our pipeline processes the reviews through 5 stages: extraction, detection, retrieval, resolution, and generation.
Get Results
Receive structured critique points, disagreement analysis, retrieved evidence, and a comprehensive meta-review.
{
"request_id": "req_1709847362.5",
"paper_title": "Learning Disentangled Representations...",
"critique_points": [
{
"Methodology": ["Promising approach to disentanglement..."],
"Experiments": ["Needs comparison with more baselines..."],
"Clarity": ["Well-written with clear motivation..."],
"Significance": ["Addresses important problem..."],
"Novelty": ["Novel use of auxiliary variables..."]
}
],
"disagreements": [
{
"review_pair": [0, 1],
"disagreement_score": 0.45,
"disagreement_details": {...}
}
],
"meta_review": "## Meta-Review\n\n### Summary..."
}Try It Now
Enter a paper's details and reviews to see the full consensus analysis pipeline in action.
Input
Results
Results will appear here after running the pipeline