Context-Value-Action Architecture for Value-Driven Large Language Model Agents
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.
69d61277c6ed9968ac5c6597
TianZe Zhang
69d61277c6ed9968ac5c6598
Sirui Sun
69d61277c6ed9968ac5c6599
Yuhang Xie
69d61277c6ed9968ac5c659a
Xin Zhang
69d61277c6ed9968ac5c659b
Zhiqiang Wu
69d61277c6ed9968ac5c659c
Guojie Song
Context-Value-Action Architecture for Value-Driven Large Language Model Agents
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.
Large language models exhibit behavioral rigidity that worsens with intensified reasoning, prompting the development of a Context-Value-Action architecture that decouples action generation from cognitive reasoning using a Value Verifier trained on human data.
large language models prompt-driven reasoning behavioral rigidity value polarization Context-Value-Action architecture Stimulus-Organism-Response model Schwartz's Theory of Basic Human Values Value Verifier CVABench real-world interaction traces