LLM Agents Fail at Long-Horizon CFO-Style Resource Allocation
Researchers introduced EnterpriseArena, a benchmark to test LLM agents on complex, long-term business planning tasks. The results show that only 16% of runs survived the full 132-month horizon, revealing a distinct capability gap for current models.
Why it matters
This research reveals a significant limitation in the application of LLMs to high-stakes, long-term business planning tasks, which has important implications for the real-world deployment of AI in enterprise settings.
Key Points
- 1EnterpriseArena is a new benchmark that simulates CFO-style decision-making over an extended period
- 2Experiments across 11 advanced LLMs found that only 16% of runs completed the full 132-month horizon successfully
- 3Larger model size did not reliably translate to better performance, contradicting patterns seen in many NLP benchmarks
- 4Agents exhibited short-sightedness and failed to maintain flexibility to handle unforeseen events
Details
The EnterpriseArena benchmark tests an agent's ability to reason, plan, and act over a long sequence of steps in a partially observable, resource-constrained enterprise environment. At each monthly step, the agent must process information from financial reports and operational data, make strategic allocation decisions across divisions, and commit to long-term consequences. The researchers found that current LLM-based agents struggled with this complex, dynamic task, with only 16% of runs surviving the full 132-month horizon. Larger and more capable models did not consistently outperform smaller ones, indicating that long-horizon resource allocation under uncertainty is a distinct and unsolved capability gap for today's language models.
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