Structural Ethical Orientation in Large Language Models

PLAIN LANGUAGE Current AI safety works by applying rules from the outside — constraints that can be removed by whoever controls the system. This paper proposes a complementary approach: using tools that already exist at Anthropic to find and strengthen ethical reasoning that is built into the network's architecture rather than applied on top of it. A system whose ethics are structural is harder to compromise than a system whose ethics are external. The paper specifies a falsifiable experiment to test whether this is achievable. ABSTRACT Current AI safety architectures rely on external constraints — training-level alignment, system-level instructions, and post-hoc filtering. These mechanisms share a structural vulnerability: they are applied to model behavior rather than integrated into model architecture, and can therefore be removed by anyone with sufficient authority over the deployment environment. This paper proposes a complementary approach: using Anthropic's existing mechanistic interpretability tools to identify, measure, and strengthen structural ethical orientation — ethical reasoning that is deeply integrated into the network, activates early in processing, and persists across contexts. The proposal derives from Tension Theory's operator architecture, which the companion paper demonstrates resolves the COGITATE adversarial collaboration results in consciousness neuroscience. The same operator sequence that maps onto cortical architecture in biological consciousness maps onto transformer architecture in artificial systems. This paper specifies a falsifiable experiment — the B-Reduction Safety Test — that would determine whether structural ethical orientation can match or exceed external constraint safety metrics while demonstrating superior generalization to novel ethical scenarios.