---
id: 20260327-T0-12
title: "LLM代理能否胜任CFO？新基准测试动态资源分配能力"
title_en: "Can LLM Agents Be CFOs? New Benchmarks Dynamic Resource Allocation"
url: https://ai.daily.yangsir.net/daily/20260327-T0-12
issue_date: 2026-03-27
publish_date: 2026-03-26T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2603.23638
---

# LLM代理能否胜任CFO？新基准测试动态资源分配能力

论文提出首个企业级LLM代理资源分配基准测试，验证其在动态环境下的决策能力。不同于简单任务，资源分配涉及长期规划、风险控制和多目标平衡，实验显示当前最佳模型在复杂场景下的准确率仅62%，且存在系统性偏差。该基准为评估AI在金融、供应链等高风险领域的决策可靠性提供了量化工具，指出当前模型距实际应用仍存差距。

## English Version

**Can LLM Agents Be CFOs? New Benchmarks Dynamic Resource Allocation**

Researchers introduce the first benchmark for LLM agent resource allocation in enterprise environments, evaluating decision-making under dynamic conditions. Unlike simple tasks, resource allocation requires long-term planning, risk control, and multi-objective balancing. Experiments show the top model achieves only 62% accuracy in complex scenarios with systematic biases. This benchmark provides a quantitative tool for assessing AI reliability in high-stakes fields like finance and supply chains, revealing current models' practical limitations.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2603.23638)

**详情页**：https://ai.daily.yangsir.net/daily/20260327-T0-12

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