Large Language Models (LLMs) are being increasingly used as autonomous agents in complex reasoning tasks, opening the niche for dialectical interactions. However, Multi-Agent systems implemented with systematically unconstrained systems systematically undergo semantic drift and logical deterioration and thus can hardly be used in providing ethical tutoring where a precise answer is required. Current simulation often tends to degenerate into dialectical stagnation, the agents degenerate into recursive concurrence or circular arguments. A critical challenge remains: how to enforce doctrinal fidelity without suppressing the generative flexibility required for dialectical reasoning? To address this niche, we contribute the Heterogeneous Debate Engine (HDE), a cognitive architecture that combines Identity-Grounded Retrieval-Augmented Generation (ID-RAG) for doctrinal fidelity and Heuristic Theory of Mind for strategic opponent modeling. Our evaluation shows that architectural heterogeneity is a crucial variable to stability: contrary doctrinal initializations (e.g., Deontology vs. Utilitarianism) have increased the Argument Complexity Scores of students by an order of magnitude, over baselines. These findings validate the effectiveness of ID-RAG and Heuristic ToM as architectural requirements in maintaining high-fidelity (adversarial) pedagogy.

翻译：大语言模型（LLM）正越来越多地被用作复杂推理任务中的自主智能体，这为辩证交互开辟了新的可能性。然而，使用系统化无约束系统实现的多智能体系统，会系统性地遭受语义漂移和逻辑退化，因此难以用于需要精确回答的伦理辅导。当前的模拟常常退化为辩证停滞，智能体陷入递归附和或循环论证。一个关键挑战依然存在：如何在辩证推理所需的生成灵活性不被抑制的情况下，强制保持教义忠实性？为应对这一挑战，我们提出了异质辩论引擎（HDE），这是一种认知架构，它结合了用于教义忠实性的基于身份锚定的检索增强生成（ID-RAG）和用于战略性对手建模的启发式心智理论。我们的评估表明，架构的异质性是稳定性的关键变量：相比基线，相反的教义初始化（例如，道义论 vs. 功利主义）使学生的论点复杂性得分提高了一个数量级。这些发现验证了ID-RAG和启发式ToM作为维护高保真度（对抗性）教学法的架构要求的有效性。