FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis

Tianshi Cai, Guanxu Li, Nijia Han, Ce Huang, Zimu Wang, Changyu Zeng, Yuqi Wang, Jingshi Zhou, Haiyang Zhang, Qi Chen, Yushan Pan, Shuihua Wang, Wei Wang

Abstract: 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.

External IDs:dblp:journals/corr/abs-2509-17395