Abstract
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.
Abstract (translated)
大型语言模型（LLMs）正日益作为自主智能体应用于复杂推理任务，从而开辟了辩证交互的细分领域。然而，采用系统化无约束方式构建的多智能体系统会系统性地经历语义漂移与逻辑退化，因此难以在需要精确答案的伦理辅导场景中应用。当前模拟往往退化为辩证停滞——智能体陷入循环共识或循环论证。一个关键挑战依然存在：如何在保障教义忠实度的同时，不抑制辩证推理所需的生成灵活性？针对这一细分领域，我们提出了异构辩论引擎（HDE），这是一种结合身份锚定检索增强生成（ID-RAG）以确保教义忠实度，以及启发式心智理论以进行策略性对手建模的认知架构。评估表明，架构异构性是稳定性的关键变量：相较于基线，对立教义初始化（如义务论与功利主义）使学生的论证复杂度评分提升了一个数量级。这些发现验证了ID-RAG与启发式心智理论作为维持高保真（对抗性）教学法的架构要求的有效性。
URL
https://arxiv.org/abs/2603.27404
