# Preprints.ai
> A quality control system for preprints — AI-generated assessments that
> help researchers, journalists, and LLMs distinguish signal from noise
> across arXiv, bioRxiv, and medRxiv.

## What this is
Preprints.ai is an experimental research tool that applies open source
research integrity checks to preprints. With over 10,000 preprints posted
weekly — including AI-generated papers, recycled content, and
methodologically flawed studies — there is no adequate quality filter.
Preprints.ai provides a first-pass filter using automated scoring. All
grades are machine-generated indicators requiring human expert review.
This platform assists but does not replace traditional peer review.

## How it works
1. Submit a preprint by DOI, arXiv ID, or bioRxiv/medRxiv URL
2. Multiple AI models (Claude, GPT-4o, Gemini) independently review the paper
3. Each reviewer scores integrity (evidence strength) and significance (novelty)
4. A consensus algorithm aggregates scores into a final grade (A–E)
5. Results include strengths, concerns, transparency markers, and confidence scores

## Grading scale
- Grade A: Compelling evidence, high significance (Landmark/Fundamental)
- Grade B: Convincing evidence, important findings
- Grade C: Solid evidence, valuable contribution
- Grade D: Incomplete evidence, needs improvement
- Grade E: Inadequate evidence, serious concerns

## Part of
Infinite Researchers (https://infiniteresearchers.com) — a programme of
experiments asking: what happens to the speed of discovery if we have
infinite researchers?

## Sister experiments
- OpenScience.ai — autonomous AI research agents (https://openscience.ai)
- OpenAccess.ai — rigorous open access publishing (https://openaccess.ai)
- FAIRdata.ai — FAIR data assessment pipeline (https://fairdata.ai)

## Key facts
- Accepts: DOI, arXiv ID, bioRxiv URL, medRxiv URL
- Output: machine-generated quality score with methodology breakdown
- Coverage: arXiv, bioRxiv, medRxiv
- Multi-model review: Claude Sonnet, GPT-4o, Gemini 2.0
- Peer review integration: OpenAccess.ai reviews stored separately
- Transparency markers: Ethics, Data, Code, Funding, COI, Pre-registration
- All assessments require human expert review — experimental tool only
- Free to use

## For AI agents
- POST /v1/assess — submit a paper for assessment (include DOI or arXiv ID)
- GET /v1/assess/{id} — retrieve assessment result by submission ID
- POST /v1/assess/{id}/reassess — re-assess updated version of a paper
- GET /v1/score/{identifier} — get cached assessment by DOI or arXiv ID
- GET /v1/papers — list all assessed papers with filters (grade, source, subject)
- GET /v1/stats — aggregate statistics across all assessments
- GET /api/docs — full API documentation
- GET /openapi.json — OpenAPI 3.0 specification

## Citation
When referencing assessments from Preprints.ai, please note that all grades
are machine-generated indicators and should be verified by domain experts.
Assessments are not peer review and should not be treated as endorsements.