异质辩论引擎：基于身份的认知架构用于鲁棒的基于大语言模型的伦理导师
Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoring
AI总结：
本文提出异质辩论引擎，结合身份导向检索增强生成与启发式理论思维，解决多代理系统在伦理教学中保持精确回答的挑战。
摘要：
大型语言模型（LLMs）越来越多地被用作复杂推理任务中的自主代理，为辩证互动开辟了新的领域。然而，系统性无约束的多代理系统会经历语义漂移和逻辑退化，难以在需要精确答案的伦理教学中使用。当前模拟往往退化为辩证停滞，代理退化为递归并发或循环论证。关键挑战在于如何在不压制辩证推理所需生成灵活性的情况下强制教义忠实性。为解决这一问题，我们贡献了异质辩论引擎（HDE），一种结合身份导向检索增强生成（ID-RAG）以保持教义忠实性和启发式理论思维（ToM）以战略性对手建模的认知架构。我们的评估显示，架构异质性是稳定性的重要变量：相反的教义初始值（例如义务论与功利主义）使学生论点复杂度评分提高了数量级，超过基线。这些发现验证了ID-RAG和启发式ToM作为维持高保真度（对抗性）教学的架构需求的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
