评分 5.7 · 来源：cs.AI updates on arXiv.org · 发布于 2026-04-08
评分依据：有一定参考价值的AI研究论文
arXiv:2604.05939v1 Announce Type: new Abstract: 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