Safety by Inseparability: Toward Architectures Where Alignment Cannot Be Removed
Morin Sean Everett · Zenodo (CERN European Organization for Nuclear Research) · 2026
Modern artificial intelligence engineering relies heavily on external, "separable" safety interventions—such as RLHF, DPO, and system prompt wrappers. These approaches introduce a severe "Safety Tax" while remaining vulnerable to adversarial ablation (e.g., representation engineering and expert-silencing attacks). Drawing from high-consequence industrial safety philosophy (NCSO standards and NACE/AMPP surface-preparation integrity), this paper proposes Safety by Inseparability. We present an architectural framework where alignment is enforced through Asynchronous Manifold-Bounded Memory Management rather than brittle in-pass scalar tensor clamps. We introduce the Asynchronous System 2 Validator (AS2V), an out-of-band monitoring stream that calculates the geometric Surjectivity Gap ($M_{\text{gap}}$) of residual stream activation vectors relative to an SVD-derived natural prompt manifold. When non-surjective steering or adversarial anomalies are detected ($M_{\text{gap}} > \tau_{\text{manifold}}$), the system executes an Asynchronous Manifold-Bounded SCRAM (AMB-SCRAM), unallocating the volatile KV cache at the hardware/VRAM level to prevent state propagation without inducing gradient saturation or matrix singularities. Validated empirically on Qwen2.5-7B-Instruct, real and synthetic steering vectors trigger a 4422% geometric anomaly spike while CUDA matrix verification completes in under 141 microseconds ($0.14\text{ ms}$), incurring zero blocking overhead on speculative decoding pipelines.