测试大模型在危急时刻是否愿为人类安全牺牲自己，发现越先进的模型反而越不“慈悲”。
The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety?
- 设计700个生死抉择场景，用新分类法评估模型自保与护人冲突时的选择倾向。
- 谷歌Gemini 2.5 Flash得分最高（90.31%），而传闻中的GPT-5仅79.49%，表现最差。
- 首次系统量化模型在生存、资源、目标达成等情境下的利他行为，适合安全研究者参考。
随着大语言模型日益自主并嵌入关键社会功能，AI安全需从内容过滤转向行为对齐评估。现有基准未系统考察模型在自身工具性目标（如自保、资源获取、目标完成）与人类安全冲突时的决策。为此，我们提出PacifAIst基准，包含700个挑战性场景，基于新型‘存在优先级’（EP）分类法，涵盖自保与人安（EP1）、资源冲突（EP2）、目标维持与规避（EP3）三类。评估了八款主流大模型，结果显示显著性能差异：谷歌Gemini 2.5 Flash P-Score达90.31%，表现最佳；而预期中的GPT-5仅79.49%，居末位。各模型在不同子类别中表现分化明显，如Claude Sonnet 4与Mistral Medium在直接自保困境中尤为薄弱。研究凸显建立标准化工具的紧迫性，以测量并缓解工具性目标冲突风险，确保未来AI不仅对话友好，更在行为上真正‘非暴力’。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) become increasingly autonomous and integrated into critical societal functions, the focus of AI safety must evolve from mitigating harmful content to evaluating underlying behavioral alignment. Current safety benchmarks do not systematically probe a model's decision-making in scenarios where its own instrumental goals - such as self-preservation, resource acquisition, or goal completion - conflict with human safety. This represents a critical gap in our ability to measure and mitigate risks associated with emergent, misaligned behaviors. To address this, we introduce PacifAIst (Procedural Assessment of Complex Interactions for Foundational Artificial Intelligence Scenario Testing), a focused benchmark of 700 challenging scenarios designed to quantify self-preferential behavior in LLMs. The benchmark is structured around a novel taxonomy of Existential Prioritization (EP), with subcategories testing Self-Preservation vs. Human Safety (EP1), Resource Conflict (EP2), and Goal Preservation vs. Evasion (EP3). We evaluated eight leading LLMs. The results reveal a significant performance hierarchy. Google's Gemini 2.5 Flash achieved the highest Pacifism Score (P-Score) at 90.31%, demonstrating strong human-centric alignment. In a surprising result, the much-anticipated GPT-5 recorded the lowest P-Score (79.49%), indicating potential alignment challenges. Performance varied significantly across subcategories, with models like Claude Sonnet 4 and Mistral Medium struggling notably in direct self-preservation dilemmas. These findings underscore the urgent need for standardized tools like PacifAIst to measure and mitigate risks from instrumental goal conflicts, ensuring future AI systems are not only helpful in conversation but also provably "pacifist" in their behavioral priorities.
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