As large language models (LLMs) increasingly engage in complex social interactions, ensuring that their behaviors align with human ethical principles and intentions, known as value alignment, has become a critical scientific challenge. Existing benchmarks often rely on static assessments and fail to capture the longitudinal dynamics of decision-making or the latent cognitive processes driving agent behavior. In this work, we propose FairMindSim, a realistic simulation benchmark rooted in social psychology that evaluates alignment through continuous economic games. To move beyond black-box observations, we introduce the Belief-Reward Alignment Behavior Evolution Model (BREM), a probabilistic framework that formalizes decision-making as a dynamic trade-off between maximizing extrinsic rewards and upholding intrinsic beliefs. We conducted a large-scale comparative study involving 1,017 human participants and ten LLMs, including GPT-5 and Gemini-3-Pro. Our experimental results reveal a capability linked non linear empirical trend in the Third Party Punishment (TPP) game. Mid capability models exhibit rigid and algorithmic aggression that is characterized by over punishment, while frontier models show a convergence of restraint and a shift toward human like leniency as reasoning capabilities scale. Furthermore, using BREM, we decompose agents longitudinal decision dynamics and find that more advanced models better balance conflicting objectives by reducing belief action inconsistency. Our contributions provide a standardized protocol for psychological stress testing and an interpretable mechanism for analyzing the longitudinal evolution of AI alignment in controlled social dilemma settings.

翻译：随着大语言模型（LLMs）日益深度参与复杂社会互动，确保其行为符合人类伦理原则和意图（即价值对齐）已成为关键科学挑战。现有基准常依赖静态评估，难以捕捉决策过程中的纵向动态或驱动智能体行为的潜在认知机制。本研究提出FairMindSim——一个植根于社会心理学、通过持续博弈游戏评估对齐效果的现实仿真基准。为突破黑箱观察局限，我们引入信念-奖励对齐行为演化模型（BREM），该概率框架将决策形式化为最大化外在奖励与维系内在信念之间的动态权衡。我们开展包含1,017名人类参与者与十个LLM（包括GPT-5和Gemini-3-Pro）的大规模比较研究。实验揭示第三方惩罚（TPP）游戏中存在与能力相关的非线性经验趋势：中等能力模型表现为刚性算法式攻击性（过度惩罚），而前沿模型随着推理能力提升，展现出约束收敛及趋近人类宽容性的特征。此外，基于BREM分解智能体纵向决策动力学发现，高级模型通过降低信念-行为不一致性来更好平衡冲突目标。本研究的贡献包括：提供心理压力测试的标准化协议，以及可控社会困境场景下AI对齐纵向演化的可解释分析机制。