FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis
This addresses financial analysis for investors, though it appears incremental as it builds on existing multi-agent and RAG techniques.
The authors tackled financial analysis by developing FinDebate, a multi-agent framework that integrates collaborative debate with domain-specific RAG, resulting in high-quality analysis with calibrated confidence levels and actionable investment strategies across multiple time horizons.
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.