Resisting Manipulative Bots in Meme Coin Copy Trading: A Multi-Agent Approach with Chain-of-Thought Reasoning

2026-01-13

AI Summary

This study addresses the challenges in meme coin copy trading—namely, the prevalence of manipulative bots, the uncertain future performance of followed wallets, and profit erosion due to transaction latency—by proposing an interpretable multi-agent system. For the first time, this work integrates multi-agent collaboration with few-shot chain-of-thought (CoT) reasoning, emulating the structure of an asset management team by decomposing trading tasks into specialized subtasks executed cooperatively. The system combines multimodal data with domain knowledge to enable highly interpretable decision-making. Evaluated on a dataset comprising 1,000 meme coin projects, the approach achieves 73% accuracy in identifying high-quality projects and 70% accuracy in detecting key opinion leader (KOL) wallets, with the selected KOLs collectively generating $500,000 in returns.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Mixture of Experts (MoE)
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 na\"ive 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.

Problem

• memecoin
• copy trading
• manipulative bots
• cryptocurrency market
• wallet performance uncertainty

Innovation

• multi-agent system
• chain-of-thought reasoning
• memecoin copy trading
• manipulative bot resistance
• explainable AI