LLaMA 3.1's Ethical Reasoning Audited for Frame-Conditioned Moral Computation

Researchers conducted a detailed audit of the LLaMA 3.1-8B-Instruct model to understand its internal ethical reasoning processes. Unlike typical behavioral audits that only observe output, this study employed an AI-driven mechanistic interpretability platform called Transluce to examine the model's computations when presented with various moral prompts, including dilemmas, policy questions, and role-playing scenarios. The audit uncovered a "Situational Anchor Effect," indicating that domain-specific representations consistently dominate the model's internal activation lists. While the model's inherent capacity for ethics-labeled concepts remains stable, their prominence and priority are significantly influenced by the specific framing of the prompt. This means the model's ethical response is largely downstream of the initial feature manifold selected by the prompt's vocabulary. The findings suggest that current behavioral alignment techniques, such as RLHF, might primarily reorder surface text without fundamentally altering the underlying domain-first computational frames. The study advocates for "Mechanistic Alignment," a research direction focused on ensuring that ethics-related features are causally privileged under varying prompt frames, rather than merely being prominent in explanations.