  This study introduces a novel Graph Neural Network (GNN) architecture that
leverages infrared and collinear (IRC) safety and equivariance to enhance the
analysis of collider data for Beyond the Standard Model (BSM) discoveries. By
integrating equivariance in the rapidity-azimuth plane with IRC-safe
principles, our model significantly reduces computational overhead while
ensuring theoretical consistency in identifying BSM scenarios amidst Quantum
Chromodynamics backgrounds. The proposed GNN architecture demonstrates superior
performance in tagging semi-visible jets, highlighting its potential as a
robust tool for advancing BSM search strategies at high-energy colliders.
