LLMs and CEO-Bench: Benchmarking Strategic Resource Reallocation

CEO-Bench is a multi-agent benchmark that evaluates LLMs on CEO-level strategic resource reallocation in a multi-round, constraint-rich organizational environment. The benchmark, by Yuyang Dai, Xueqing Peng, Lingfei Qian and Zhuohan Xie, was submitted to arXiv on 16 Jun 2026 (arXiv:2606.17459) and spans 13 pages.

What is CEO-Bench?

CEO-Bench is a structured evaluation framework that places LLM agents in the role of a CEO who must synthesize conflicting recommendations from four role-conditioned C-suite advisors: CFO, CTO, COO and CMO. The advisors hold private signals and distinct priorities; the LLM must produce a concrete allocation plan evaluated along four dimensions: role integration, conditional boldness, history-sensitive judgment, and plan validity.

The benchmark simulates a multi-round resource reallocation process under organizational constraints and information asymmetry. Scenarios require the LLM to reconcile advisor trade-offs across time, producing allocations that obey structural constraints while answering higher-order strategic questions about when and how to reallocate capital between business units.

How do models perform on CEO-Bench?

Across five frontier models and 13 scenarios, all tested models achieve high structural validity but diverge sharply on strategic calibration, the authors report. Experiments reveal systematic failure modes: single-advisor capture, conservative default under ambiguity, and historical amnesia.

The paper highlights a structural integration-boldness tradeoff: models that engage more deeply with conflicting perspectives tend to produce less decisive action. In other words, deeper engagement with advisor arguments correlates with lower conditional boldness. Those are the core empirical findings reported across the five frontier models the authors evaluated.

Why it matters

The benchmark separates syntactic or structural competence from the strategic judgment executives need. High structural validity means models can produce technically valid plans, but strategic calibration governs whether those plans meaningfully reallocate resources in response to competing stakeholder signals and evolving history. That gap matters for any deployment that positions LLMs as decision-support or decision-making agents in organizational settings, because unchecked failure modes like advisor capture or historical amnesia could produce systematically biased or timid resource moves.

Designers of AI-assisted executive systems should therefore measure and mitigate strategic calibration, not only structural correctness. The findings suggest evaluation and training must explicitly reward calibrated boldness and memory of past rounds if LLMs will be trusted with multi-round strategic allocation.

What to watch

Look for follow-up work that reports per-model breakdowns of strategic calibration across scenarios, methods that reduce single-advisor capture without sacrificing integration, and extensions of CEO-Bench beyond the four C-suite roles the authors use. A concrete next milestone is models that demonstrate both deep role integration and decisive conditional boldness across multiple rounds.

Notes and provenance The paper was submitted to arXiv on 16 Jun 2026 as arXiv:2606.17459, authored by Yuyang Dai, Xueqing Peng, Lingfei Qian and Zhuohan Xie, and includes 13 pages of methods, scenarios and experimental results. The benchmark’s core dimensions are role integration, conditional boldness, history-sensitive judgment and plan validity, and the simulated advisor roles are CFO, CTO, COO and CMO.