Design of AI Agent Models Driven by Multimodal Data
[Purpose/Significance]This study develops an AI agent model for multimodal data processing across varied tasks.By exploring the inherent value of multimodal data,the model enhances the support for de-cision-making processes.[Method/Process]This study designed a hybrid AI agent model that integrated both horizontal and vertical structures.Using the financial annual reports from thirty Dow Jones Industrial Average companies as a case study,this model facilitated intelligent investment research and analysis for the year 2023.The research compared the performance of different organizational structures of financial agents in executing three sub-tasks:analysis of company fundamentals,market sentiment,and risk and opportunity assessment.Decisions were made based on these analyses,leveraging large language models capable of processing multimodal data.[Result/Conclusion]The vertical agent structure excels in tasks requiring efficient,directional strategic analysis with clear hierarchical decision-making.Conversely,the horizontal structure is more effective in scenarios demanding collabo-ration,flexibility,and broad expertise.The hybrid model outperforms single-structure agents in terms of accuracy in the final investment decision,demonstrating its efficacy in optimizing task-specific agent configurations.
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