We present "Bot Wars," a framework using Large Language Models (LLMs) scam-baiters to counter phone scams through simulated adversarial dialogues. Our key contribution is a formal foundation for strategy emergence through chain-of-thought reasoning without explicit optimization. Through a novel two-layer prompt architecture, our framework enables LLMs to craft demographically authentic victim personas while maintaining strategic coherence. We evaluate our approach using a dataset of 3,200 scam dialogues validated against 179 hours of human scam-baiting interactions, demonstrating its effectiveness in capturing complex adversarial dynamics. Our systematic evaluation through cognitive, quantitative, and content-specific metrics shows that GPT-4 excels in dialogue naturalness and persona authenticity, while Deepseek demonstrates superior engagement sustainability.

本研究针对电话诈骗问题，提出了“机器人战争”框架，通过使用大型语言模型的骗子诱捕者，进行模拟对抗对话。该框架的新颖之处在于通过链条思维推理，实现无明确优化的战略出现，并展示了GPT-4在对话自然性和角色真实感方面的卓越表现，具有潜在的显著影响。

进化的机器人战争：在反击电话诈骗中的竞争大型语言模型的协调