Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation
arXiv:2606.17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings. These evaluations overlook the defining challenge of real executive decision-making: integrating conflicting recommendations from specialized stakeholders under information asymmetry, organizational constraints, and temporal dependencies. We introduce \textsc{CEO-Bench}, a multi-agent benchmark tha
The rapid advancement in large language models requires more sophisticated evaluations, especially as their capabilities approach real-world complex decision-making scenarios.
This benchmark directly addresses the critical question of whether AI can autonomously manage high-level strategic functions, moving beyond isolated tasks to integrated executive decision-making.
The focus of LLM evaluation is shifting from specialized cognitive tasks to multi-stakeholder strategic decision-making, which better reflects real-world executive challenges.
- · AI Agent Developers
- · Companies adopting AI for strategic roles
- · AI research institutions
- · Traditional management consulting firms
- · Companies resistant to AI integration
- · Human executive assistants
The development of more robust and reliable AI models capable of complex strategic roles accelerates.
Organizational structures within companies may begin to fundamentally change to accommodate AI 'CEOs' or high-level strategic agents.
The definition of human leadership and its indispensable qualities will be critically re-evaluated in the face of highly capable AI executives.
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Read at arXiv cs.AI