Polarization in online discourse erodes social trust and accelerates misinformation, yet technical responses remain largely diagnostic and post-hoc. Current governance approaches suffer from inherent latency and static policies, struggling to counter coordinated adversarial amplification that evolves in real-time. We present EvoCorps, an evolutionary multi-agent framework for proactive depolarization. EvoCorps frames discourse governance as a dynamic social game and coordinates roles for monitoring, planning, grounded generation, and multi-identity diffusion. A retrieval-augmented collective cognition core provides factual grounding and action--outcome memory, while closed-loop evolutionary learning adapts strategies as the environment and attackers change. We implement EvoCorps on the MOSAIC social-AI simulation platform for controlled evaluation in a multi-source news stream with adversarial injection and amplification. Across emotional polarization, viewpoint extremity, and argumentative rationality, EvoCorps improves discourse outcomes over an adversarial baseline, pointing to a practical path from detection and post-hoc mitigation to in-process, closed-loop intervention. The code is available at https://github.com/ln2146/EvoCorps.

翻译：在线话语中的极化侵蚀社会信任并加速错误信息传播，然而技术应对措施大多仍停留在诊断性和事后处理层面。现有治理方法存在固有延迟和静态策略缺陷，难以应对实时演变的协同对抗性放大行为。本文提出EvoCorps——一种主动消除话语极化的演化多智能体框架。该框架将话语治理构建为动态社会博弈，协调监控、规划、基于事实的生成及多身份传播等角色。检索增强的集体认知核心提供事实依据和行动-结果记忆，而闭环演化学习机制能随环境与攻击者变化自适应调整策略。我们在MOSAIC社会人工智能仿真平台上实现了EvoCorps，通过在包含对抗性注入与放大的多源新闻流中进行受控评估。在情感极化、观点极端性和论证理性三个维度上，EvoCorps相较于对抗基线显著改善了话语生态，为从检测与事后缓解转向过程内闭环干预提供了可行路径。代码已开源：https://github.com/ln2146/EvoCorps。