LLM CFO Benchmark: EnterpriseArena Launched

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📄Read original on ArXiv AI

#llm-agents#resource-allocation#enterprise-benchmarkenterprisearenaenterprisearena

💡New benchmark shows top LLMs fail as CFOs—only 16% survive 132 months

⚡ 30-Second TL;DR

What Changed

Introduces EnterpriseArena benchmark for CFO-style resource allocation

Why It Matters

Reveals critical limitations in current LLM agents for enterprise tasks, prioritizing research into robust long-term decision-making. May shift focus from short-horizon tasks to dynamic resource management in business AI applications.

What To Do Next

Download EnterpriseArena from arXiv and benchmark your LLM agent on resource allocation tasks.

Who should care:Researchers & Academics

Key Points

•Introduces EnterpriseArena benchmark for CFO-style resource allocation
•132-month simulator with partial observability via budgeted tools
•11 LLMs tested: only 16% survive full horizon, no reliable scaling
•Identifies long-horizon planning as key LLM agent gap

🧠 Deep Insight

AI-generated analysis for this event — not the original article.

🔑 Enhanced Key Takeaways

•EnterpriseArena utilizes a dynamic, multi-agent simulation framework where LLMs must manage competing business units, requiring them to balance short-term liquidity against long-term strategic growth targets.
•The benchmark incorporates a 'budgeted tool' mechanism, forcing agents to trade off the cost of information acquisition (e.g., querying market reports) against the potential utility of that information for decision-making.
•Analysis of the 16% success rate indicates that failure is primarily driven by 'compounding error propagation,' where early suboptimal resource allocations lead to irreversible financial insolvency in later simulation stages.

📊 Competitor Analysis▸ Show

Benchmark	Focus Area	Primary Metric	Environment Complexity
EnterpriseArena	CFO/Resource Allocation	Survival Rate/ROI	High (132-month, partial observability)
GAIA	General AI Assistants	Task Completion	Medium (Real-world web tools)
AgentBench	Multi-environment	Success Rate	Medium (Diverse, isolated tasks)
SWE-bench	Software Engineering	Code Resolution	Low (Isolated repo-level tasks)

🛠️ Technical Deep Dive

•Environment: Built on a custom discrete-event simulation engine that models stochastic macro-economic variables (inflation, interest rates) and firm-specific operational shocks.
•Observability: Implements a 'fog of war' system where agents receive incomplete financial statements and must explicitly invoke API-like tools to retrieve granular data, incurring a simulated 'time/cost' penalty.
•Evaluation Metric: Uses a composite score of 'Survival Duration' (months until bankruptcy) and 'Cumulative Economic Value Added' (EVA) to normalize performance across different agent strategies.
•Agent Interface: Supports standard ReAct and Plan-and-Solve prompting patterns, with a constrained action space limited to capital expenditure, debt issuance, and dividend policy.

🔮 Future ImplicationsAI analysis grounded in cited sources

EnterpriseArena will become a standard stress-test for long-context LLMs.

The benchmark's requirement for maintaining state over 132 simulated months forces developers to prioritize long-term memory architectures over simple retrieval-augmented generation.

Future agent training will shift toward 'cost-aware' planning.

The high failure rate in EnterpriseArena highlights that agents lacking an internal model of resource-constrained information gathering cannot function in complex, high-stakes enterprise environments.

⏳ Timeline

2026-02

Initial release of EnterpriseArena benchmark on ArXiv.

📄Read original article on ArXiv AI

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