Jun 1, 2026 Safety Conclusion
RRbench (ResearchBench): Trust-first verification for scientific research
Citations are not enough
Modern AI papers ship with code, weights, and datasets — but traditional signals like citations and venue say little about whether the work reproduces. RRbench starts from that gap.
The platform extracts claims, evaluates evidence across papers and artifacts, and synthesizes a verdict — with every step auditable.
Four signals, one score
RRbench combines AI agents (claim extraction, evidence evaluation), artifact integrity (SHA-256 verification, immutable audit logs), expert and community review (threaded reviews, professor and admin workflows), and a transparent 0–100 Trust Score.
The score is evidence-backed and decomposable: no single signal — citations included — decides alone.
Status and access
RRbench is in early phase with a promising verdict: the preview is public, the repository is open, and launch updates ship through News.
Built for AI/ML researchers and institutions who need faster, clearer credibility assessment beyond traditional metrics.
Appendix: methods & artifacts — Conclusion · Jun 1, 2026 · 8 min read · Filed under Safety, Conclusion.
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