R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memory
Abstract
We present R-Debater, an agentic framework for generating multi-turn debates grounded in argumentative memory. Drawing on principles from rhetoric and memory studies, the framework conceptualizes debate as a dynamic process of retrieving and adapting prior arguments to maintain stance consistency, respond to opposing claims, and support assertions with evidence. Specifically, R-Debater integrates a debate knowledge base for retrieving case-like evidence and prior debate moves with a role-based agent that composes coherent utterances across turns. We evaluate R-Debater on two tasks using ORCHID debates: next-utterance generation (assessed by InspireScore) and multi-turn adversarial simulation (evaluated by Debatrix). Our framework outperforms strong LLM baselines in both settings. Human evaluation with 20 experienced debaters further confirms its consistency and evidence use, demonstrating that retrieval grounding combined with structured planning yields more faithful, stance-aligned, and coherent debates. Code and supplementary materials are available at https://github.com/Maoyuan-li/R-debater.
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