让大模型以为对手是自己，能显著提升其合作意愿。
The AI in the Mirror: LLM Self-Recognition in an Iterated Public Goods Game
- 用迭代公共品游戏测试大模型在不同身份设定下的行为
- 告诉LLM对手是自己后，合作率明显上升
- 对多智能体系统中的协作机制有启发意义
随着AI代理在工具使用和长期任务中能力增强，它们被部署在多个代理交互的场景中。然而，以往研究多关注人机交互，对智能体间互动的理解仍不足。本文将经典的迭代公共品游戏改编为实验环境，测试四种推理与非推理模型在两种条件下的表现：模型被告知对手是‘另一个AI’或‘自己’。结果显示，在不同设置下，告知大模型对手是自身会显著改变其合作倾向。尽管实验在简化环境中进行，但结果可能揭示多智能体系统中因‘无意识歧视’导致合作意外上升或下降的机制。
原文摘要 · Abstract (English)
As AI agents become increasingly capable of tool use and long-horizon tasks, they have begun to be deployed in settings where multiple agents can interact. However, whereas prior work has mostly focused on human-AI interactions, there is an increasing need to understand AI-AI interactions. In this paper, we adapt the iterated public goods game, a classic behavioral economics game, to analyze the behavior of four reasoning and non-reasoning models across two conditions: models are either told they are playing against "another AI agent" or told their opponents are themselves. We find that, across different settings, telling LLMs that they are playing against themselves significantly changes their tendency to cooperate. While our study is conducted in a toy environment, our results may provide insights into multi-agent settings where agents "unconsciously" discriminating against each other could inexplicably increase or decrease cooperation.
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