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arXiv · 2602.00061

Simple Role Assignment is Extraordinarily Effective for Safety Alignment

Zhou Ziheng·Jiakun Ding·Zhaowei Zhang·Ruosen Gao·Yingnian Wu·Demetri Terzopoulos·Yipeng Kang·Fangwei Zhong·Junqi Wang
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Abstract

Principle-based alignment often lacks context sensitivity and completeness. Grounded in Theory of Mind, we propose role conditioning as a compact alternative: social roles (e.g., mother, judge) implicitly encode both values and the cognitive schemas required to apply them. We introduce a training-free pipeline featuring a role-conditioned generator and iterative role-based critics for refinement. Across five model families, our approach consistently outperforms principle-based, Chain-of-Thought (CoT) and other baselines across benchmarks. Notably, it reduces unsafe outputs on the WildJailbreak benchmark from 81.4\% to 3.6\% with DeepSeek-V3. Not only for common safety benchmarks, it consistently applies for agentic safety tasks. These results establish role assignment as a powerful, interpretable paradigm for AI alignment and LLM-as-a-Judge construction.

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Zhou Ziheng, Jiakun Ding, Zhaowei Zhang, Ruosen Gao, Yingnian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong, Junqi Wang. 2026-01-20. Simple Role Assignment is Extraordinarily Effective for Safety Alignment. https://arxiv.org/abs/2602.00061

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