Trait Activation in Silicon: A Situation-Aware Framework for Psychologically Grounded Role-Playing
Zuolong Li University of Science and Technology of China, Pingyu Wu University of Science and Technology of China, Xianwen Huang, Tianyi Wei Microsoft, Wenbo Zhou University of Science and Technology of China
Role-playing agents (RPAs) have made significant strides in mimicking static character identities. However, their personality simulations remain superficial, lacking a profound understanding of complex human psychological mechanisms. We identify a critical bottleneck termed “Personality Inertia”—a behavioral rigidity where RLHF-induced alignment bias traps models in a sanitized, “helpful assistant” persona. This inertia prevents models from adapting to diverse social contexts or expressing essential but negative traits under pressure. To bridge this gap, we propose PD-LLM, a situation-aware framework grounded in Trait Activation Theory. PD-LLM introduces Bipolar Latent Decomposition, which decouples personality traits into bidirectional LoRA adapters. These adapters are dynamically modulated by a situation-aware module based on the DIAMONDS taxonomy, allowing for precise behavioral regulation. Empirical results show that while baseline methods fail to synchronize multidimensional traits under pressure, PD-LLM achieves superior performance in both static fidelity and dynamic adaptability. By advancing from prompt engineering to intrinsic parameter control, PD-LLM effectively overcomes personality rigidity, facilitating the creation of vivid and psychologically consistent agents.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表