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From 97% Model Accuracy to 74% Clinical Reliability: Building RSN-NNSL-GATE-001
Scientific & BioAI Infrastructure
RExSyn Nexus-Bio

From 97% Model Accuracy to 74% Clinical Reliability: Building RSN-NNSL-GATE-001

Learn how RSN-NNSL-GATE-001 turns high model accuracy into system-level clinical reliability by blocking unsafe AI pipeline decisions, measuring end-to-end risk, and enforcing fail-closed governance.

Evidence-aware scientific systems#AI#AI Alignment#AI Governance#Biomedical#Bioinformatics#Mlops#Deep Learning#Machine Learning#Cognitive Science#Scientific Integrity#AI Research#Architecture
When Adding Chai-1 and Boltz-2 Exposed Hidden Model Disagreement(Trinity Protocol Part)
Scientific & BioAI Infrastructure
RExSyn Nexus-Bio

When Adding Chai-1 and Boltz-2 Exposed Hidden Model Disagreement(Trinity Protocol Part)

See how adding Chai-1 and Boltz-2 to an AlphaFold workflow exposed hidden model disagreement, increased drift, and revealed why failed convergence can be the most valuable signal in computational biology.

Evidence-aware scientific systems#AI#Biomedical#Bioinformatics#Mlops#AI Research#Scientific Integrity#Architecture
Turning a Research Paper into a Runnable System
AI Governance Systems
Governed Reasoning

Turning a Research Paper into a Runnable System

Turn a research paper into a runnable system. This article shows how HRPO’s core equations were implemented with bounded policy lag, KL rejection, and execution checks to test real-world fidelity.

Control, auditability, and safe boundaries#AI#AI Ethics#AI Alignment#AI Governance#Deep Learning#Machine Learning#SR9/DI2#AI Research#Scientific Integrity#AI Code#Architecture#Contextengineering
The Paper That Runs Itself: Why AI-Era Research Must Be Code-Native
Scientific & BioAI Infrastructure

The Paper That Runs Itself: Why AI-Era Research Must Be Code-Native

Literate Programming, DevOps for Research, Research Infrastructure, Continuous Integration, AI Transparency, Scientific Publishing, arXiv Culture

Evidence-aware scientific systems#AI Research#Scientific Integrity

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