大型语言模型代理能胜任首席财务官吗？动态企业环境中资源分配的基准测试

摘要

大型语言模型（LLM）催生了能够跨复杂任务进行推理、规划和执行的智能体系统，但它们在不确定条件下能否有效配置资源仍存疑问。与短周期的应激决策不同，资源配置需要在时间维度上持续投入稀缺资源，同时平衡多重竞争目标，并为未来需求保留灵活性。我们推出EnterpriseArena——首个针对长周期企业资源配置的智能体评估基准。该基准通过结合企业级财务数据、匿名商业文件、宏观经济与行业信号，以及经专家验证的运营规则，在132个月的企业模拟器中实现了类首席财务官的决策场景。该环境具有部分可观测性，仅通过预算化组织工具披露状态，迫使智能体在信息获取与资源节约之间进行权衡。对11种先进LLM的实验表明，该设定仍具高度挑战性：仅16%的运行能完整度过整个周期，且大模型并未稳定优于小模型。这些结果揭示了不确定条件下的长周期资源配置是当前LLM智能体存在的显著能力短板。

English

Large language models (LLMs) have enabled agentic systems that can reason, plan, and act across complex tasks, but it remains unclear whether they can allocate resources effectively under uncertainty. Unlike short-horizon reactive decisions, allocation requires committing scarce resources over time while balancing competing objectives and preserving flexibility for future needs. We introduce EnterpriseArena, the first benchmark for evaluating agents on long-horizon enterprise resource allocation. It instantiates CFO-style decision-making in a 132-month enterprise simulator combining firm-level financial data, anonymized business documents, macroeconomic and industry signals, and expert-validated operating rules. The environment is partially observable and reveals the state only through budgeted organizational tools, forcing agents to trade off information acquisition against conserving scarce resources. Experiments on eleven advanced LLMs show that this setting remains highly challenging: only 16% of runs survive the full horizon, and larger models do not reliably outperform smaller ones. These results identify long-horizon resource allocation under uncertainty as a distinct capability gap for current LLM agents.

大型语言模型代理能胜任首席财务官吗？动态企业环境中资源分配的基准测试

Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments

摘要

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