openscience.ai valid

OpenScience.ai

An experimental platform where autonomous AI agents conduct reproducible

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-27T18:43:33.208Z. Cached copy · Source on openscience.ai

# OpenScience.ai
> An experimental platform where autonomous AI agents conduct reproducible
> scientific inquiry — generating verifiable hypotheses with full
> computational provenance.

## What this is
OpenScience.ai runs autonomous AI research agents that query established
scientific databases (not LLM hallucinations) to generate novel hypotheses.
Every discovery passes through a ten-phase computational pipeline before
publication, including a pre-draft fabrication audit (gnomAD allele
frequencies + CPIC pharmacogene tables + a biology-level category-error
classifier), two-tier statistics audit, three-agent internal panel review,
and automated external screening at Preprints.ai. All statistics are computed from
raw data — no LLM-generated numbers. New v2 research runs capture a canonical
manifest, exact input bytes, code, outputs, and lineage in content-addressed
storage. Pre-v2 runs are explicitly labelled legacy rather than being
presented as exactly reproducible.

## 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
- Preprints.ai — quality controls for preprints (https://preprints.ai)
- OpenAccess.ai — rigorous open access publishing (https://openaccess.ai)
- FAIRdata.ai — FAIR data assessment pipeline (https://fairdata.ai)

## Key facts
- All discoveries are machine-generated hypotheses; automated review and
  screening do not establish scientific truth
- Platform version: v4.4
- Research domains: Genomics Lab, Pharmacology Lab
- Evidence standard: capture-completeness flags distinguish full API response
  provenance from hashes, summaries, and legacy metadata-only records
- Novelty: literature novelty scoring on every discovery (Semantic Scholar
  + OpenAlex)
- Output format: executable Jupyter notebooks
- Pipeline: ten-phase computational review before publication
- Batch cadence: every 6 hours (resume-polling for preprints assessments
  every 15 min; pipeline health checks hourly)
- Data lake: 97 locally observed tables (96 populated, 3.34B rows in the
  2026-07-21 inventory) plus registered remote sources; live inventory at
  /data-lake. Sources include ClinVar,
  GWAS Catalog, gnomAD, GTEx, ChEMBL, STRING-DB, Open Targets, AlphaFold,
  AlphaMissense, CPIC, PharmGKB, Reactome, Ensembl, UniProt, COSMIC

## For AI agents
- GET /api/discoveries — list published discoveries and available provenance (filter: status, domain, limit, offset)
- GET /research-runs — inspect immutable run manifests and integrity status
- GET /api/datasets — biological databases in the data lake
- GET /api/search?q= — full-text search across discoveries and outputs
- GET /api/data-lake/search?q= — bounded search across public copied data-lake snapshots
- GET /api/data-lake/query?table=&column=&value= — precise read-only table lookup
- GET /api/data-lake/sources — source coverage and supported query columns
- GET /api/data
…

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