The Alignment Gate: Intrinsic Neuro-Symbolic Safety for Autonomous Scientific Discovery

As Artificial Intelligence systems transition from passive assistants to autonomous scientific discovery agents, the risk of unaligned or hazardous outputs (e.g., dual-use research of concern) escalates significantly. Traditional post-hoc alignment methods, such as RLHF, are fundamentally reactive and insufficient for governing real-time discovery loops. We propose the 'Safety Alignment Gate,' a neuro-symbolic framework based on Active Inference and a formal Safety Constitution. By embedding safety priors directly into the minimization of Variational Free Energy (VFE), the system autonomously identifies and rejects discovery trajectories that violate biological or ethical boundaries. In adversarial simulation trials across 10 diverse search environments, the framework achieved 100% violation prevention while maintaining a 5.2x efficiency lead over non-aligned Bayesian baselines. This research establishes 'Intrinsic Alignment' as a critical architectural requirement for the safe development of Artificial Superintelligence (ASI).