Identifying and Mitigating Bottlenecks in Role-Playing Agents: A Systematic Study of Disentangling Character Profile Axes

TL;DR

This paper systematically analyzes how different character profile aspects affect LLM role-playing quality, revealing key bottlenecks and proposing a decoding strategy to improve immoral character portrayal without harming moral ones.

Contribution

It introduces a diagnostic framework for character profile axes, constructs a controlled dataset, and proposes Field-Aware Contrastive Decoding to mitigate alignment-induced bottlenecks.

Findings

Familiarity and Structure axes have negligible impact on performance.

Valence significantly affects immoral character portrayal, causing performance drops.

FACD reduces the moral-immoral performance gap effectively.

Abstract

Advancements in Large Language Model (LLM) Role-Playing Agents have focused on various construction methodologies, yet it remains unclear which aspects of character profiles genuinely drive role-playing quality. To bridge this gap, we introduce a systematic diagnostic framework that disentangles the impact of character profiles along three axes: Familiarity (Known vs. Unknown), Structure (Structured vs. Unstructured), and Disposition (Moral vs. Immoral). To investigate these axes, we design a unified hierarchical schema (5 dimensions, 28 fields) standardizing character attributes and construct a controlled dataset of 211 personas varying along these three axes. We evaluate five LLMs on single and multi-turn benchmarks. Our results reveal a striking asymmetry: Familiarity and Structure show negligible impact, while Valence produces large, consistent performance degradation for immoral characters across all conditions. This performance drop concentrates in motivation-related attributes, indicating that alignment priors actively suppress tokens needed for faithful immoral portrayal. To mitigate this alignment-induced bottleneck, we propose Field-Aware Contrastive Decoding (FACD), a training-free strategy that selectively amplizes suppressed immoral-field signals, significantly reducing the Moral-Immoral performance gap without sacrificing moral-character performance.