✨OASIS: Open Agent Social Interaction Simulations with One Million Agents

📝 Summary:
OASIS is a scalable and generalizable social media simulator that models up to one million LLM agents. It replicates complex social phenomena like information spreading and group polarization across platforms, demonstrating enhanced group dynamics and diverse opinions with larger agent groups.

🔹 Publication Date: Published on Nov 18, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2411.11581
• PDF: https://arxiv.org/pdf/2411.11581
• Github: https://github.com/camel-ai/oasis

✨ Spaces citing this paper:
• https://huggingface.co/spaces/nguyenthanhasia/oasis-demo

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#LLMAgents #SocialSimulation #AgentBasedModeling #ComputationalSocialScience #GroupDynamics

✨When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation

📝 Summary:
Reasoning-enhanced LLMs can over-optimize, making them better problem solvers but poor simulators of diverse, boundedly rational behavior. This solver-sampler mismatch means high model capability hurts simulation fidelity. Bounded reflection improves realism.

🔹 Publication Date: Published on Apr 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.11840
• PDF: https://arxiv.org/pdf/2604.11840
• Project Page: https://www.sandric.co

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#LLM #MultiAgentSystems #BehavioralSimulation #AI #AgentBasedModeling

🔥 TradingAgents: Multi-Agents LLM Financial Trading Framework

💡 The paper introduces TradingAgents, a multi-agent framework that utilizes large language models for stock trading, simulating the collaborative dynamics of real-world trading firms. The framework consists of various agents, including fundamental analysts, sentiment analysts, technical analysts, and traders with different risk profiles, all powered by large language models. These agents work together to assess market conditions, manage risk, and make informed trading decisions. The framework also includes researcher agents that evaluate market conditions and a risk management team that monitors exposure.

The authors propose this framework as a solution to the limitations of existing single-agent systems and multi-agent frameworks that gather data independently. By simulating a dynamic and collaborative trading environment, TradingAgents aims to improve trading performance metrics such as cumulative returns and Sharpe ratio.

The results of the experiments show that the TradingAgents framework outperforms baseline models, with significant improvements in cumulative returns, Sharpe ratio, and maximum drawdown. The framework is made available to the public, demonstrating the potential of multi-agent large language model frameworks in financial trading. Overall, the paper contributes to the development of more sophisticated and collaborative trading systems, inspired by the dynamics of real-world trading firms.

📅 Published on Dec 28, 2024

🔗 Links:
• arXiv: https://arxiv.org/abs/2412.20138
• PDF: https://arxiv.org/pdf/2412.20138
• GitHub: https://github.com/tauricresearch/tradingagents ⭐ 66.0k

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
• https://huggingface.co/spaces/tahp0604/ai-stock-watchlist
• https://huggingface.co/spaces/Ervin2077/qiu

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📢 By: https://xn--r1a.website/PaperNexus

#MultiAgentSystems #LargeLanguageModels #FinancialTrading #ArtificialIntelligenceInFinance #AgentBasedModeling

🔥 Very Large-Scale Multi-Agent Simulation in AgentScope

💡 The paper addresses the challenges of conducting large scale multi agent simulations with existing platforms, which include limited scalability, low efficiency, and effort intensive management processes. To overcome these challenges, the authors enhance the AgentScope platform by introducing several new features and components. They propose an actor based distributed mechanism to improve scalability and efficiency, and provide flexible environment support to simulate various real world scenarios. This allows for parallel execution of multiple agents, centralized workflow orchestration, and interactions among agents. The authors also integrate a configurable tool and an automatic background generation pipeline to simplify the process of creating agents with diverse background settings. Additionally, they provide a web based interface for monitoring and managing a large number of agents across multiple devices. The authors conduct a comprehensive simulation to demonstrate the effectiveness of the proposed enhancements and release the source code on GitHub to inspire further research and development in large scale multi agent simulations. The results show the great potential of applying multi agent systems in large scale simulations, and the enhancements to AgentScope improve its convenience and flexibility for supporting very large scale multi agent simulations.

📅 Published on Jul 25, 2024

🔗 Links:
• arXiv: https://arxiv.org/abs/2407.17789
• PDF: https://arxiv.org/pdf/2407.17789
• GitHub: https://github.com/modelscope/agentscope ⭐ 24.6k

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📢 By: https://xn--r1a.website/PaperNexus

#MultiAgentSimulation #AgentBasedModeling #DistributedSimulation #ScalableComputing #ParallelProcessing