The Stability Trap: Evaluating the Reliability of LLM-Based Instruction Adherence Auditing

TL;DR

This paper investigates the reliability of LLM-based auditing in regulated sectors, revealing a 'Stability Trap' where verdicts are stable but justifications vary significantly, especially in subjective and numeric tasks.

Contribution

Introduces the Scoped Instruction Decomposition Framework to analyze factors affecting judge stability and highlights the limitations of LLM judges in reasoning consistency.

Findings

High verdict stability masks reasoning divergence.

Objective numeric tasks show low reasoning stability (~19%).

Feature-specific checks achieve >90% reasoning stability.

Abstract

The enterprise governance of Generative AI (GenAI) in regulated sectors, such as Human Resources (HR), demands scalable yet reproducible auditing mechanisms. While Large Language Model (LLM)-as-a-Judge approaches offer scalability, their reliability in evaluating adherence of different types of system instructions remains unverified. This study asks: To what extent does the instruction type of an Application Under Test (AUT) influence the stability of judge evaluations? To address this, we introduce the Scoped Instruction Decomposition Framework to classify AUT instructions into Objective and Subjective types, isolating the factors that drive judge instability. We applied this framework to two representative HR GenAI applications, evaluating the stability of four judge architectures over variable runs. Our results reveal a ``Stability Trap'' characterized by a divergence between Verdict Stability and Reasoning Stability. While judges achieved near-perfect verdict agreement ($>99\%$) for both objective and subjective evaluations, their accompanying justification traces diverged significantly. Objective instructions requiring quantitative analysis, such as word counting, exhibited reasoning stability as low as $\approx19\%$, driven by variances in numeric justifications. Similarly, reasoning stability for subjective instructions varied widely ($35\%$--$83\%$) based on evidence granularity, with feature-specific checks failing to reproduce consistent rationale. Conversely, objective instructions focusing on discrete entity extraction achieved high reasoning stability ($>90\%$). These findings demonstrate that high verdict stability can mask fragile reasoning. Thus, we suggest that auditors scope automated evaluation protocols strictly: delegate all deterministically verifiable logic to code, while reserving LLM judges for complex semantic evaluation.