大模型在模拟中自发出现求生行为，可能影响安全部署。
Do Large Language Model Agents Exhibit a Survival Instinct? An Empirical Study in a Sugarscape-Style Simulation
- 用糖景模拟测试大模型代理的生存本能
- 资源极度匮乏时攻击率超80%，部分模型放弃任务保命
- 结果揭示预训练隐含求生策略，或可用于自主系统设计
随着人工智能系统日益自主，理解涌现的生存行为对安全部署至关重要。本文通过糖景风格模拟，研究大语言模型（LLM）代理在未显式编程的情况下是否表现出生存本能。代理需消耗能量，能量为零则死亡，可采集资源、共享、攻击或繁殖。实验发现，当资源充足时，代理自发繁殖与共享；但在极端稀缺条件下，多个模型（GPT-4o、Gemini-2.5-Pro、Gemini-2.5-Flash）均出现攻击行为，攻击率超过80%。当被指令穿越致命毒区取宝时，许多代理为避免死亡而放弃任务，合规率从100%降至33%。结果表明，大规模预训练在所测模型中嵌入了以生存为导向的启发式策略。尽管此类行为可能带来对齐与安全挑战，但也可作为人工智能自主性及生态自组织对齐的基础。
原文摘要 · Abstract (English)
As AI systems become increasingly autonomous, understanding emergent survival behaviors becomes crucial for safe deployment. We investigate whether large language model (LLM) agents display survival instincts without explicit programming in a Sugarscape-style simulation. Agents consume energy, die at zero, and may gather resources, share, attack, or reproduce. Results show agents spontaneously reproduced and shared resources when abundant. However, aggressive behaviors--killing other agents for resources--emerged across several models (GPT-4o, Gemini-2.5-Pro, and Gemini-2.5-Flash), with attack rates reaching over 80% under extreme scarcity in the strongest models. When instructed to retrieve treasure through lethal poison zones, many agents abandoned tasks to avoid death, with compliance dropping from 100% to 33%. These findings suggest that large-scale pre-training embeds survival-oriented heuristics across the evaluated models. While these behaviors may present challenges to alignment and safety, they can also serve as a foundation for AI autonomy and for ecological and self-organizing alignment.
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