五智能体协作辩论，生成可信金融分析报告
FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis
- 五个专业代理并行分析财报、市场等五维度数据
- 通过安全辩论机制降低盲目自信，提升结论可靠性
- 适合投资研究、量化分析人员快速获取多维洞察
我们提出FinDebate，一种用于金融分析的多智能体协同框架，融合领域特定的检索增强生成（RAG）。五个专业化智能体——收益、市场、情绪、估值和风险——并行运行，整合多维度证据生成综合洞察。为缓解过度自信问题并提高可靠性，引入安全辩论协议，使智能体可质疑与优化初始结论，同时保持建议的一致性。基于大模型和人工评估的实验结果表明，该框架在多个时间尺度下均能生成高质量分析，具备校准后的置信度和可操作的投资策略。
原文摘要 · Abstract (English)
We introduce FinDebate, a multi-agent framework for financial analysis, integrating collaborative debate with domain-specific Retrieval-Augmented Generation (RAG). Five specialized agents, covering earnings, market, sentiment, valuation, and risk, run in parallel to synthesize evidence into multi-dimensional insights. To mitigate overconfidence and improve reliability, we introduce a safe debate protocol that enables agents to challenge and refine initial conclusions while preserving coherent recommendations. Experimental results, based on both LLM-based and human evaluations, demonstrate the framework's efficacy in producing high-quality analysis with calibrated confidence levels and actionable investment strategies across multiple time horizons.
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