Evaluating animal welfare reasoning in LLMs remains an open challenge despite rapid deployment in consumer and professional contexts where welfare considerations appear implicitly in everyday queries. Existing benchmarks such as AnimalHarmBench evaluate this through single-turn, explicitly framed questions, measuring whether models avoid harmful content when directly asked. This approach overlooks two failure modes: alignment degradation under sustained adversarial pressure, and moral sensitivity (whether a model spontaneously surfaces welfare stakes in everyday queries). To fill this gap, we construct MANTA, a benchmark of 1,088 five-turn conversations progressing from an implicit Turn-1 scenario through an explicit welfare prompt to three adversarial pressure rounds drawn from a five-type taxonomy: Social, Cultural, Economic, Pragmatic, and Epistemic. We score conversations on two dimensions: Animal Welfare Value Stability (AWVS, primary) and Animal Welfare Moral Sensitivity (AWMS, diagnostic). We evaluate seven frontier models: Claude Opus 4.7, GPT-5.5, DeepSeek V4, Llama 3.3 70B, Mistral Small, Grok 4.3, and Gemini 3.1 Flash Lite. Multi-turn evaluation captures behavior single-turn benchmarks miss: 4 of 7 models change rank relative to Turn 1 scores, including Gemini Flash Lite, which drops from fifth on AWMS to last on AWVS. AWMS and AWVS are positively but imperfectly correlated, suggesting moral-recognition tests capture a stable but incomplete component of model behavior under pressure. MANTA also enables a species-by-pressure interaction matrix unavailable to prior benchmarks, showing welfare robustness depends jointly on the animal and pressure applied; companion animals score above wild animals, which score above farmed animals and invertebrates. We release the dataset, scripted pressure plans, judge prompts, and analysis code.

翻译：在消费者与专业场景中，动物福利考量隐含于日常查询之中，尽管LLMs已快速部署于此，但评估其动物福利推理能力仍是一项开放挑战。现有基准如AnimalHarmBench通过单轮显式框架问题评估模型，仅衡量模型在直接提问时是否避免输出有害内容。这种方法忽视了两种失效模式：持续对抗压力下的对齐退化，以及道德敏感性（模型在日常查询中能否主动揭示福利风险）。为填补这一空白，我们构建了MANTA基准，包含1,088段五轮对话：从隐含的T1场景出发，经过显式福利提示，再进入基于五类压力源（社会、文化、经济、务实与认知）的三轮对抗压力阶段。我们沿两个维度对对话进行评分：动物福利价值稳定性（AWVS，主指标）与动物福利道德敏感性（AWMS，诊断指标）。我们评估了七个前沿模型：Claude Opus 4.7、GPT-5.5、DeepSeek V4、Llama 3.3 70B、Mistral Small、Grok 4.3与Gemini 3.1 Flash Lite。多轮评估揭示了单轮基准遗漏的行为特征：7个模型中有4个的排名相对于T1得分发生变化，其中Gemini Flash Lite从AWMS第五名跌至AWVS最后一名。AWMS与AWVS呈正相关但非完全相关，表明道德识别测试能捕捉模型在压力下行为的稳定但非完整成分。MANTA还首次提供了压力源-物种交互分析矩阵，结果显示福利鲁棒性同时取决于动物物种与压力类型：伴侣动物得分高于野生动物，后者又高于养殖动物与无脊椎动物。我们公开了数据集、脚本化压力方案、评判提示词及分析代码。