提出CVA架构，让大模型智能体行为更真实多样。
Context-Value-Action Architecture for Value-Driven Large Language Model Agents
- 用价值验证器分离思考与行动，模拟人类动态价值观。
- 在110万真实交互数据上，行为多样性提升且偏差减少。
- 适合追求真实、可解释智能体的研究者和开发者。
大语言模型虽能模拟人类行为，但现有智能体常显僵化，这一问题在依赖自我评判的评估中被掩盖。通过真实世界数据验证，我们发现强化提示驱动的推理反而加剧价值极化，导致群体多样性坍塌。为此，我们提出基于S-O-R模型和舒瓦茨基本价值观理论的上下文-价值-行动（CVA）架构。CVA通过在真实人类数据上训练的价值验证器，将行动生成与认知推理解耦，显式建模动态价值激活。在包含超过110万条真实交互轨迹的CVABench数据集上的实验表明，该方法显著优于基线，在降低极化的同时提升了行为保真度与可解释性。
原文摘要 · Abstract (English)
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.
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