AI RESEARCH
Context-Value-Action Architecture for Value-Driven Large Language Model Agents
arXiv CS.AI
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ArXi:2604.05939v1 Announce Type: new 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.