LMAgent is a large-scale multimodal agents society that simulates complex social systems using multimodal LLMs, enabling realistic e-commerce behavior simulations with over 10,000 concurrent agents.
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https://arxiv.org/abs/2412.09237
🤔 Original Problem:
Existing LLM-based multi-agent systems are limited in scale and modality, failing to capture the complexity of real-world social interactions.
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🛠️ Solution in this Paper:
→ LMAgent creates a society of AI agents with unique personas, fast memory mechanisms, and multimodal interaction capabilities.
→ It introduces a self-consistency prompting mechanism to enhance agents' multimodal decision-making.
→ The system employs a fast memory mechanism with a three-tier structure: sensor, short-term, and long-term memory.
→ A small-world network model initializes agent relationships, improving communication efficiency.
→ Agents can engage in various e-commerce behaviors, including browsing, purchasing, and live streaming.
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💡 Key Insights from this Paper:
→ Multimodal LLMs can simulate complex social systems at scale
→ Self-consistency prompting improves agent decision-making
→ Fast memory mechanisms enhance system efficiency
→ Small-world networks model realistic social structures
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📊 Results:
→ Supports over 10,000 concurrent agent simulations
→ 40% improvement in system efficiency with fast memory
→ 29.34% improvement over baseline methods in purchase prediction
→ Comparable performance to humans in behavioral indicators
→ Successfully simulates complex phenomena like herd behavior
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