---
id: 20260326-T0-18
title: "金融文档处理：多代理LLM架构性能对比研究"
title_en: "Multi-Agent LLM Benchmark for Financial Document Processing"
url: https://ai.daily.yangsir.net/daily/20260326-T0-18
issue_date: 2026-03-26
publish_date: 2026-03-25T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2603.22651
---

# 金融文档处理：多代理LLM架构性能对比研究

最新研究全面对比了多代理LLM架构在金融文档处理中的表现，分析了不同编排模式的成本-准确率权衡。研究团队测试了6种主流架构，发现协作式代理在保持90%准确率的情况下，计算成本降低了40%。该研究为金融AI系统架构设计提供了重要参考，特别适合处理复杂的监管文档。

## English Version

**Multi-Agent LLM Benchmark for Financial Document Processing**

A new study comprehensively benchmarks multi-agent LLM architectures for financial document processing, analyzing cost-accuracy tradeoffs across orchestration patterns. Testing six mainstream architectures, researchers found collaborative agents reduce computation costs by 40% while maintaining 90% accuracy. This provides crucial guidance for financial AI system design, especially for complex regulatory documents.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2603.22651)

**详情页**：https://ai.daily.yangsir.net/daily/20260326-T0-18

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