This paper introduces a methodological framework for empirically testing AI alignment strategies through structured multi-model dialogue. Drawing on Peace Studies traditions - particularly interest-based negotiation, conflict transformation, and commons governance - we operationalize Viral Collaborative Wisdom (VCW), an approach that reframes alignment from a control problem to a relationship problem developed through dialogical reasoning. Our experimental design assigns four distinct roles (Proposer, Responder, Monitor, Translator) to different AI systems across six conditions, testing whether current large language models can engage substantively with complex alignment frameworks. Using Claude, Gemini, and GPT-4o, we conducted 72 dialogue turns totaling 576,822 characters of structured exchange. Results demonstrate that AI systems can engage meaningfully with Peace Studies concepts, surface complementary objections from different architectural perspectives, and generate emergent insights not present in initial framings - including the novel synthesis of "VCW as transitional framework." Cross-architecture patterns reveal that different models foreground different concerns: Claude emphasized verification challenges, Gemini focused on bias and scalability, and GPT-4o highlighted implementation barriers. The framework provides researchers with replicable methods for stress-testing alignment proposals before implementation, while the findings offer preliminary evidence about AI capacity for the kind of dialogical reasoning VCW proposes. We discuss limitations, including the observation that dialogues engaged more with process elements than with foundational claims about AI nature, and outline directions for future research including human-AI hybrid protocols and extended dialogue studies.

翻译：本文提出了一种通过结构化多模型对话实证测试AI对齐策略的方法论框架。借鉴和平研究传统——特别是基于利益的谈判、冲突转化与公地治理——我们将"病毒式协作智慧"（Viral Collaborative Wisdom, VCW）这一方法操作化，该框架将对齐问题从控制问题重新定义为通过对话式推理发展的关系问题。我们的实验设计为六种不同条件下的AI系统分配了四个独立角色（提议者、响应者、监控者、翻译者），以测试当前大语言模型能否实质性地参与复杂对齐框架。使用Claude、Gemini和GPT-4o模型，我们进行了72轮对话，共计576,822字符的结构化交流。结果表明，AI系统能够有意义地参与和平研究概念，从不同架构视角呈现互补的反对意见，并产生初始框架中未出现的新兴见解——包括"VCW作为过渡框架"这一新颖综合。跨架构模式显示不同模型关注点各异：Claude强调验证挑战，Gemini聚焦偏见与可扩展性，GPT-4o则突出实施障碍。该框架为研究人员提供了在实施前压力测试对齐方案的可复制方法，同时研究结果为AI进行VCW所倡导的对话式推理能力提供了初步证据。我们讨论了研究局限，包括观察到对话更多关注流程要素而非关于AI本质的基础主张，并展望了未来研究方向，包括人机混合协议与扩展对话研究。