Read the paper like a reviewer
Our system ingests the full manuscript and extracts the methods, assumptions, inputs, outputs, and dependencies that matter for reproduction.
Agentic reproducibility intelligence
We turn scientific papers into auditable reproducibility evidence.
Reproduc-io helps fix the broken review process behind the reproducibility crisis. In collaboration with the European Commission's Joint Research Centre, we built an agentic LLM pipeline that reads a paper, decomposes it into a workflow, and scores how reproducible each step truly is from the information provided.
- Paper in structured workflow out
- Stepwise scoring for transparent review signals
- Built for scale from pilot studies to publication pipelines
Assessment snapshot
74%
Data acquisition
Documented
Preprocessing
Partially specified
Model training
Missing hyperparameters
Evaluation
Insufficient benchmark detail
The pipeline highlights reproducibility gaps before reviewers have to.
Designed for journals, funders, publishers, policy teams, and research integrity groups.
The platform
From dense paper to decision-grade reproducibility signal
Convert narrative into workflow
The agentic pipeline transforms prose into an explicit workflow so every step can be inspected, verified, and challenged.
Score each step for reproducibility
Instead of a vague yes or no, we assess whether each part of the workflow is reproducible based on evidence actually present in the paper.
Support reviewers with actionable gaps
Missing details, ambiguous procedures, and weak reporting become explicit signals that can guide editors, reviewers, and authors.
Why now
The scientific record needs stronger gatekeeping
The problem
Reviewers face impossible workloads, methods sections remain opaque, and reproducibility often gets judged with too little time and too little structure.
The opportunity
Agentic AI can bring consistency, scale, and traceability to review workflows, turning reproducibility from an aspiration into an operational check.
What comes next
A broader reproducibility stack for modern research
Automated reproducibility assessment
A first-line AI gatekeeper that inspects papers before or during review.
Paper-to-code reproduction pipeline
Automatically attempt full reproduction and generate executable code artifacts.
Reference and citation verification
Detect citation issues, unsupported claims, and broken evidence chains.
Author guidance for better reproducibility
Suggest targeted improvements so manuscripts become easier to trust and reuse.
"Better science starts with better evidence at review time."
Reproduc-io
Build the next generation of scientific review with us.
We’re creating infrastructure for trustworthy research evaluation. If you work in publishing, policy, funding, or research integrity, let's talk.