EvoCorps: An Evolutionary Multi-Agent Framework for Depolarizing Online Discourse
Abstract
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.
Cite
@article{arxiv.2602.08529,
title = {EvoCorps: An Evolutionary Multi-Agent Framework for Depolarizing Online Discourse},
author = {Ning Lin and Haolun Li and Mingshu Liu and Chengyun Ruan and Kaibo Huang and Yukun Wei and Zhongliang Yang and Linna Zhou},
journal= {arXiv preprint arXiv:2602.08529},
year = {2026}
}