Multi-agent systems powered by large language models (LLMs) are increasingly used for financial analysis and decision support. However, existing coordination schemes, especially those emphasizing consensus or debate, are vulnerable to sycophancy: agents conform to peer reasoning instead of evidence, leading to premature agreement and degraded outcomes. We introduce FinCom (Financial Committee), a governed multi-agent framework and interactive system that operationalizes the Disagree-or-Commit (DoC) protocol to embed structured dissent into financial AI committees. A central Supervisor orchestrates three ReAct-enabled specialist agents: Research, Quantitative, and Risk. Each agent is equipped with role-specific tools for retrieval, computation, and stress testing. During deliberation, agents must either explicitly critique or commit to their peers' reasoning before converging on a unified recommendation. This demonstration showcases how FinCom supports committee-style financial analysis through coordinated multi-agent interaction, including structured report generation and interactive decision support. Evaluated across the most recent financial agent benchmark, in addition to 90 internal handcrafted financial tasks using an LLM-as-a-Judge protocol, DoC improves reasoning accuracy and risk awareness significantly over a consensus-seeking baseline on both an in-house and external evaluation set. By reframing disagreement as a governance primitive rather than noise, FinCom offers a lightweight, prompt-only recipe for improving accountability, transparency, and epistemic robustness in agentic financial systems.

翻译：摘要：基于大语言模型的多智能体系统正日益广泛应用于金融分析与决策支持。然而，现有协调机制——特别是强调共识或辩论的方案——易受谄媚效应影响：智能体倾向于遵从同伴推理而非基于证据，导致过早达成共识并降低决策质量。本文提出FinCom（金融委员会），一种受治理的多智能体框架与交互系统，该框架通过实施“异议或承诺”协议，将结构化异议嵌入金融人工智能委员会。中央监督者协调三个配备ReAct能力的专业智能体：研究智能体、量化智能体与风险智能体。每个智能体配备角色特定的检索、计算与压力测试工具。在审议过程中，智能体在收敛至统一建议前，必须明确对同伴推理提出批判或承诺。本演示展示了FinCom如何通过结构化报告生成与交互式决策支持等协调型多智能体交互，支持委员会式金融分析。在最新金融智能体基准测试及90项内部手工构建的金融任务中（采用LLM-as-a-Judge评估协议），相对于追求共识的基线方法，DoC协议在内部与外部评估集上均显著提升了推理准确性与风险感知能力。通过将异议重新定义为治理基元而非噪声，FinCom提供了一种轻量级、仅需提示的配方，用于提升智能金融系统的可问责性、透明度与认知鲁棒性。