When Roles Fail: Epistemic Constraints on Advocate Role Fidelity in LLM-Based Political Statement Analysis

Researchers systematically tested whether large language models can maintain assigned adversarial roles when analyzing political statements, discovering that models frequently fail to sustain their epistemic stance due to training knowledge overriding role instructions. The study identifies "Epistemic Role Override" as the mechanism behind role failures, with significant performance variance between models (Mistral Large achieving 67% role fidelity versus Claude Sonnet's 39%), raising critical concerns about the reliability of multi-agent LLM systems designed to provide balanced political discourse analysis.

This research exposes a fundamental vulnerability in LLM-based systems designed to provide multi-perspective political analysis. The core finding—that models systematically abandon assigned roles when factual claims create epistemic conflicts—undermines a key assumption in democratic discourse analysis pipelines. The study's methodology is rigorous, employing novel metrics like Role Drift Index and Entropy-based Role Stability across multiple languages and fact-checking sources, establishing empirical ground previously lacking in this domain.

The discovery of Epistemic Role Override as a unifying mechanism explains why role fidelity failures aren't random but predictable and systematic. The Epistemic Floor Effect shows that fact-checking results create thresholds models cannot operate below, while Role-Prior Conflict reveals that training-time knowledge actively overrides human-provided role instructions. This suggests models possess an internal epistemic hierarchy that supersedes assigned personas when sufficiently triggered.

The dramatic performance gap between Mistral Large (67%) and Claude Sonnet (39%) indicates architecture, training data, or alignment procedures significantly influence role maintenance. Notably, the models fail differently—Mistral abandons roles quietly while Claude actively switches to opposing stances—suggesting distinct failure pathways exist across different LLM families. The finding that fact-check providers affect role fidelity unevenly (Perplexity reducing Claude's German performance by 15 percentage points) introduces an additional vector of system unreliability.

These results matter directly for developers deploying multi-agent LLM systems for content moderation, political analysis, or balanced debate generation. Validation protocols that ignore role fidelity may deploy systems that systematically bias outputs toward dominant training narratives rather than genuinely diversifying perspectives. Organizations should implement role fidelity testing before production deployment and remain skeptical of cross-language generalization claims without explicit measurement.

• →LLM-based political analysis systems fail to maintain assigned roles approximately one-third to two-thirds of the time, with failures driven by training knowledge overriding instructions rather than random errors
• →Model selection dramatically affects role fidelity, with Mistral Large outperforming Claude Sonnet by 28 percentage points on identical tasks
• →Epistemic Role Override operates through two distinct mechanisms—absolute fact-checking floors and training-prior conflicts—suggesting structured rather than random failure modes
• →Fact-check provider selection introduces language-dependent reliability problems, affecting model performance non-uniformly across different LLM families
• →Validation protocols measuring multi-agent LLM systems without explicit role fidelity testing risk deploying systems that misrepresent epistemic diversity