{"ID":2867512,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17395","arxiv_id":"2509.17395","title":"FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis","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.","short_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....","url_abs":"https://arxiv.org/abs/2509.17395","url_pdf":"https://arxiv.org/pdf/2509.17395v1","authors":"[\"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\"]","published":"2025-09-22T06:56:27Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\"]","has_code":false}