| Title: |
FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis |
| Authors: |
Cai, Tianshi; Li, Guanxu; Han, Nijia; Huang, Ce; Wang, Zimu; Zeng, Changyu; Wang, Yuqi; Zhou, Jingshi; Zhang, Haiyang; Chen, Qi; Pan, Yushan; Wang, Shuihua; Wang, Wei |
| Publication Year: |
2025 |
| Collection: |
ArXiv.org (Cornell University Library) |
| Subject Terms: |
Computation and Language |
| Description: |
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. ; Accepted at FinNLP@EMNLP 2025. Camera-ready version |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
http://arxiv.org/abs/2509.17395 |
| Availability: |
http://arxiv.org/abs/2509.17395 |
| Accession Number: |
edsbas.B12EDF0 |
| Database: |
BASE |