RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents

RPA-Check introduces an automated four-stage framework for evaluating Large Language Model-based Role-Playing Agents in complex scenarios, addressing the gap in standard NLP metrics for assessing role adherence and narrative consistency. Testing across legal scenarios reveals that smaller, instruction-tuned models (8-9B parameters) outperform larger models in procedural consistency, suggesting optimal performance doesn't correlate with model scale.

The emergence of LLM-based role-playing agents has created a critical evaluation bottleneck. Traditional NLP benchmarks measure surface-level linguistic quality but fail to capture whether agents maintain character consistency, follow procedural constraints, or sustain logical coherence over extended interactions. RPA-Check addresses this by combining human-defined behavioral criteria with LLM-as-a-judge verification, creating a reproducible assessment methodology for specialized domains.

This research reflects growing recognition that model capability differs from model reliability in constrained environments. The legal scenario testing reveals a counterintuitive finding: smaller models with focused instruction-tuning demonstrate superior procedural adherence compared to larger architectures prone to sycophancy and user-alignment bias. This challenges the prevailing assumption that scaling alone improves performance and has profound implications for resource efficiency in enterprise AI deployments.

For developers building specialized agents—whether in legal, medical, or financial domains—the framework provides tangible guidance on model selection and evaluation methodology. Organizations can now quantify trade-offs between computational cost and behavioral fidelity rather than relying on subjective assessments. The research validates that parameter count matters far less than alignment precision when role-adherence and consistency are critical requirements.

Looking forward, standardized evaluation frameworks like RPA-Check will become essential as enterprises demand auditable, interpretable AI systems. The methodology's applicability extends beyond gaming and legal training to customer service, content moderation, and regulatory compliance scenarios where agent behavior must be predictable and defensible.

• →RPA-Check provides a standardized four-stage framework for objectively evaluating LLM-based agents in constraint-heavy environments.
• →Smaller 8-9B parameter models demonstrate superior procedural consistency compared to larger models despite lower theoretical capability.
• →The framework combines behavioral criteria definition, semantic filtering, and chain-of-thought LLM judging for reproducible assessments.
• →Testing across five legal scenarios reveals an inverse relationship between model scale and operational stability in specialized domains.
• →Standardized agent evaluation metrics address a critical gap between general-purpose LLM benchmarks and domain-specific reliability requirements.