Large Language Models (LLMs) have shown promise in simulating human behavior, yet existing agents often exhibit behavioral rigidity, a flaw frequently masked by the self-referential bias of current "LLM-as-a-judge" evaluations. By evaluating against empirical ground truth, we reveal a counter-intuitive phenomenon: increasing the intensity of prompt-driven reasoning does not enhance fidelity but rather exacerbates value polarization, collapsing population diversity. To address this, we propose the Context-Value-Action (CVA) architecture, grounded in the Stimulus-Organism-Response (S-O-R) model and Schwartz's Theory of Basic Human Values. Unlike methods relying on self-verification, CVA decouples action generation from cognitive reasoning via a novel Value Verifier trained on authentic human data to explicitly model dynamic value activation. Experiments on CVABench, which comprises over 1.1 million real-world interaction traces, demonstrate that CVA significantly outperforms baselines. Our approach effectively mitigates polarization while offering superior behavioral fidelity and interpretability.

翻译：大语言模型在模拟人类行为方面展现出前景，然而现有智能体常表现出行为僵化，这一缺陷常被当前“LLM即评判者”评估中的自我参照偏见所掩盖。通过对照经验事实真相进行评测，我们揭示了一个反直觉现象：增加提示驱动推理的强度非但不会提升保真度，反而加剧了价值极化，导致群体多样性崩溃。为解决这一问题，我们提出语境-价值-行动架构，该架构基于刺激-有机体-反应模型与施瓦茨人类基本价值观理论。与依赖自我验证的方法不同，CVA通过一种新型价值验证器将行动生成与认知推理解耦，该验证器基于真实人类数据训练，显式建模动态价值激活。在包含超过110万条真实世界交互轨迹的CVABench上进行的实验表明，CVA显著优于基线方法。我们的方法有效缓解了极化现象，同时提供了卓越的行为保真度与可解释性。