Online group chats are social spaces with local conversational norms that are rarely stated explicitly. The ability and willingness of LLM-based agents to recognize and adapt to these norms remains mostly unexplored. We introduce LoSoNA, a benchmark for local social norm adaptation in multi-party chat. Each scenario gives a subject model a curated group-chat transcript in which non-subject participants demonstrate a hidden local norm, followed by a final elicitor turn that forces a response revealing whether the subject has inferred that norm. We evaluate eight frontier and open-weight models under four prompting conditions that vary how explicitly the model is told to treat the prior conversation as evidence for how it should answer. Naive prompting remains limited for most models; explicit norm-aware prompting helps unevenly, with Gemini 3.1 Pro reaching $84.2\%$ and Claude Fable 5 reaching $81.6\%$, while several other models show small gains or regressions. LoSoNA contributes to recent calls for evaluating LLM social capabilities by testing whether models can infer local conversational norms from precedent and use them in a one-turn group-chat response.

翻译：在线群聊是具有局部会话规范的社会空间，但这些规范极少被明确表述。基于大语言模型的智能体识别并适应这些规范的能力与意愿在很大程度上尚未被探索。我们提出LoSoNA——一个面向多方聊天中局部社会规范适应的基准测试。每个场景向目标模型提供经过策划的群聊记录，其中非目标参与者展示了一种隐含的局部规范，随后通过最终触发轮次迫使模型作出回应，从而揭示其是否推断出该规范。我们在四种提示条件下评估了八个前沿模型与开源模型，这些条件在要求模型将先前对话视为回答依据的明确程度上有所差异。对大多数模型而言，朴素提示仍存在局限性；显式规范感知提示的帮助效果参差不齐：Gemini 3.1 Pro达到84.2%，Claude Fable 5达到81.6%，而其他若干模型仅小幅提升甚至出现倒退。LoSoNA通过测试模型能否从先例中推断局部会话规范并在单轮群聊回复中加以应用，顺应了近期关于评估大语言模型社交能力的呼吁。