PurenetX is built on peer-reviewed research principles. We publish findings transparently and seek academic validation before making public claims.
A structured classification of contextual signals relevant to visual safety decisions. Covers scene type, activity, relationship, intent, platform, and risk dimensions.
Publication status: In preparation. Targeting arXiv pre-print after pilot validation.
A curated dataset of visual scenarios annotated with contextual signals, risk labels, and correct decisions. Designed for fairness testing across demographics.
Status: Dataset collection in progress. Initial version expected Q4 2026.
PurenetX Context Recognition Benchmark — a peer-reviewed evaluation framework for context-aware AI safety systems. Designed to be platform-neutral and openly reproducible.
Status: Design phase. Targeting academic co-authorship for peer review.
Our internal methodology for fairness evaluation, bias testing, and responsible deployment. We document limitations as carefully as capabilities.
Status: Internal framework complete. Public report planned after pilot validation.
We are preparing our first technical publications. We follow the principle that numbers without peer review mislead — so we publish after validation, not before.
Our primary technical paper introducing the Context Intelligence Layer architecture, the CKB dataset, and initial validation results from enterprise pilots.
Expected: After enterprise pilot validation completes · arXiv + conference submission
Introducing the PurenetX Context Recognition Benchmark — evaluation methodology, test scenarios, and fairness criteria for context-aware AI systems.
Expected: 2027 · Targeting peer-reviewed venue
We're seeking university research partners for co-authorship and independent validation. Contact us at purenetx.ai@gmail.com
Honest accounting of what the system does and does not do well — the same approach leading AI labs publish in their model cards.
PurenetX is actively seeking university and research lab collaborations for independent validation, co-authorship, and benchmark development.
We welcome academic co-authors for our forthcoming papers. Your independent analysis strengthens the work.
Approved research partners receive access to the CKB dataset for independent study under standard research agreements.
Seeking institutions to co-develop and independently validate the PCRB benchmark before public release.