CEO-Bench: Can Agents Play the Long Game?

arXiv:2606.18543v2 Announce Type: replace Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. An agent manages pricing, marketing, budgeting, and many other aspects of a fictional company through a programmable Python interface, operating in the same environment and facing the same challenges as a human CEO. Success demands analyzing noisy, interconnected business databases, translating signals into sound strategy, and coordinating many decisions with programming. The strongest agents write sophisticated code that forecasts churn regimes, billing timing, customer losses, and future cash under different scenarios. Even so, most state-of-the-art models struggle in this environment. Only Claude Fable 5, GPT-5.6 Sol, and Claude Opus 4.8 finish above the $1M starting balance, and all evaluated models remain below the rule-based baseline. CEO-Bench takes a first step toward measuring the intelligence required to drive sustained, adaptive progress over time.

Title:CEO-Bench: Can Agents Play the Long Game?

Abstract:Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. An agent manages pricing, marketing, budgeting, and many other aspects of a fictional company through a programmable Python interface, operating in the same environment and facing the same challenges as a human CEO. Success demands analyzing noisy, interconnected business databases, translating signals into sound strategy, and coordinating many decisions with programming. The strongest agents write sophisticated code that forecasts churn regimes, billing timing, customer losses, and future cash under different scenarios. Even so, most state-of-the-art models struggle in this environment. Only Claude Fable 5, GPT-5.6 Sol, and Claude Opus 4.8 finish above the $1M starting balance, and all evaluated models remain below the rule-based baseline. CEO-Bench takes a first step toward measuring the intelligence required to drive sustained, adaptive progress over time.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)

arXiv:2606.18543 [cs.AI]

(or arXiv:2606.18543v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.18543 Focus to learn more arXiv-issued DOI via DataCite

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CEO-Bench: Can Agents Play the Long Game?

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