Negotiation is a core component of social intelligence, requiring agents to balance strategic reasoning, cooperation, and social norms. Recent work shows that LLMs can engage in multi-turn negotiation, yet nearly all evaluations occur exclusively in English. Using controlled multi-agent simulations across Ultimatum, Buy-Sell, and Resource Exchange games, we systematically isolate language effects across English and four Indic framings (Hindi, Punjabi, Gujarati, Marwadi) by holding game rules, model parameters, and incentives constant across all conditions. We find that language choice can shift outcomes more strongly than changing models, reversing proposer advantages and reallocating surplus. Crucially, effects are task-contingent: Indic languages reduce stability in distributive games yet induce richer exploration in integrative settings. Our results demonstrate that evaluating LLM negotiation solely in English yields incomplete and potentially misleading conclusions. These findings caution against English-only evaluation of LLMs and suggest that culturally-aware evaluation is essential for fair deployment.

翻译：谈判是社会智能的核心组成部分，要求智能体在策略推理、合作与社会规范之间取得平衡。近期研究表明，大语言模型能够进行多轮谈判，但几乎所有评估都仅使用英语进行。通过在最后通牒、买卖和资源交换游戏中开展受控多智能体模拟，我们在保持游戏规则、模型参数和激励条件不变的前提下，系统性地分离了英语与四种印度语言框架（印地语、旁遮普语、古吉拉特语、马尔瓦迪语）的语言效应。研究发现，语言选择对结果的影响可能比更换模型更为显著，能够逆转提议者优势并重新分配剩余价值。关键的是，这种效应具有任务依赖性：印度语言会降低分配型博弈的稳定性，却在整合型情境中引发更丰富的探索行为。我们的结果表明，仅使用英语评估大语言模型的谈判能力会得出不完整且可能具有误导性的结论。这些发现警示了仅依赖英语评估大语言模型的局限性，并表明文化感知的评估对于公平部署至关重要。