Context-Value-Action Architecture for Value-Driven Large Language Model Agents
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhang, TianZe Sun, Sirui Xie, Yuhang Zhang, Xin Wu, Zhiqiang Song, Guojie |
| author_facet | Zhang, TianZe Sun, Sirui Xie, Yuhang Zhang, Xin Wu, Zhiqiang Song, Guojie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05939 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context-Value-Action Architecture for Value-Driven Large Language Model Agents Zhang, TianZe Sun, Sirui Xie, Yuhang Zhang, Xin Wu, Zhiqiang Song, Guojie Artificial Intelligence Human-Computer Interaction 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. |
| title | Context-Value-Action Architecture for Value-Driven Large Language Model Agents |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.05939 |