抵制操纵机器人在表情包加密货币复制交易中的应用：一种基于多智能体的链式推理方法
Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning
AI总结：
本文提出一种基于多智能体和链式推理的复制交易系统，以抵御操纵机器人，通过多模态大语言模型提升预测准确度和经济表现，实现加密货币投资的稳健收益。
摘要：
加密货币市场中复制交易已成为主导的入场策略。然而，由于市场极端不流动性及波动性，该策略暴露了可被利用的攻击面：攻击者部署操纵机器人来抢先执行交易、隐藏仓位并伪造情绪，系统性地从 naive 的复制者中大规模提取价值。尽管其普及性，由机器人驱动的操纵行为仍鲜有研究，且缺乏稳健的防御框架。我们提出了一种基于多智能体架构的操纵-resistant 复制交易系统，该系统由多模态大语言模型（LLM）和链式推理（CoT）驱动。我们的方法在预测准确度以及经济表现上均优于零样本和大多数统计驱动的基线，在现实市场摩擦下，实现每只加密货币投资平均复制者收益为3%。整体而言，我们的结果展示了基于智能体的防御效果，并预测了在对抗性加密货币市场中交易者的盈利能力，为稳健的复制交易提供了实用基础。
英文摘要：
Copy trading has become the dominant entry strategy in meme coin markets. However, due to the market's extremely illiquid and volatile nature, the strategy exposes an exploitable attack surface: adversaries deploy manipulative bots to front-run trades, conceal positions, and fabricate sentiment, systematically extracting value from naïve copiers at scale. Despite its prevalence, bot-driven manipulation remains largely unexplored, and no robust defensive framework exists. We propose a manipulation-resistant copy-trading system based on a multi-agent architecture powered by a multi-modal large language model (LLM) and chain-of-thought (CoT) reasoning. Our approach outperforms zero-shot and most statistic-driven baselines in prediction accuracy as well as all baselines in economic performance, achieving an average copier return of 3% per meme coin investment under realistic market frictions. Overall, our results demonstrate the effectiveness of agent-based defenses and predictability of trader profitability in adversarial meme coin markets, providing a practical foundation for robust copy trading.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
