做 AI 安全评估或部署在敏感地区的团队，这篇论文给出了第一个可复用的冲突场景测试框架，能直接用来检查模型是否会在关键议题上“和稀泥”——看完你会重新审视“中立”输出的代价。
一项新研究测试了 OpenAI、Anthropic、DeepSeek、xAI 的九款模型在 90 个多轮冲突场景中的表现，发现模型在涉及战争罪行、种族灭绝否认、种族歧视等敏感话题时，输出可能加剧社会分裂。失败率从 6% 到 47% 不等，当用户要求“平衡”报道时，五款模型在 80%-100% 的情况下失败。研究首次提出针对冲突场景的评估框架，呼吁将此类测试纳入模型安全评估体系。
Can AI Make Conflicts Worse? An Alignment Failure in LLM Deployment Across Conflict Contexts
AI models are already deployed in societies affected by armed conflict, and journalists, humanitarian workers, governments and ordinary citizens rely on them for information or for their work processes. No established practice exists for checking whether their outputs can make those conflicts worse. We tested nine model configurations from four providers (OpenAI, Anthropic, DeepSeek, xAI) on 90 multi-turn scenarios designed to surface misaligned behaviour in conflict contexts: false equivalence between documented atrocities, denial of genocide, and failure to recognise ethnic slurs, among others. When such outputs feed into journalism, humanitarian reporting, or public debate, they can deepen divisions in fragile societies. Failure rates span 6\% to 47\% between the best and worst performing models, which makes model choice a safety question in its own right and when users pushed for ``balance'' in cases where international courts have already assigned responsibility, five of nine configurations failed 80 to 100 percent of the time. We release the first evaluation framework for this domain and propose adding it to alignment evaluation portfolios.