Human-Alignment Metrics for Safe Instruction Following AI Systems

Abstract

Human alignment in instruction-following AI systems depends not only on generating correct final
outcomes but on maintaining the fidelity of the reasoning process that leads to those outcomes. As
models interpret and decompose user instructions into internal inference steps, subtle forms of
reasoning drift can emerge, producing outputs that are fluent yet misaligned with user intent. This work
introduces a reasoning-trace-based alignment framework that evaluates alignment as a property of the
inference pathway rather than the generated response alone. The method captures step-by-step
reasoning sequences, measures semantic coherence and structural task correspondence, and computes
an Alignment Integrity Index that reflects instruction-faithful reasoning stability. Experimental results
show that alignment breakdowns follow predictable patterns such as structural drift, semantic
misprioritization, context-release collapse, and shallow reasoning compression. By mapping these
failure modes to targeted stabilization strategies, the proposed approach provides a reproducible and
operational method for detecting, diagnosing, and correcting misalignment in advanced instruction
following AI systems.