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arxiv: 2606.17459 · v1 · pith:45XRV5CRnew · submitted 2026-06-16 · 💻 cs.AI

Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

Yuyang Dai , Xueqing Peng , Lingfei Qian , Zhuohan Xie This is my paper

Pith reviewed 2026-06-27 01:40 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM evaluationmulti-agent simulationstrategic decision-makingresource allocationCEO benchmarkinformation asymmetryorganizational constraints
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The pith

LLMs achieve high structural validity in CEO resource allocation but diverge sharply on strategic calibration when synthesizing conflicting advisor inputs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CEO-Bench to test whether LLMs can perform executive-level strategic resource reallocation by integrating conflicting advice from four role-conditioned advisors each holding private signals. Experiments on five frontier models across 13 scenarios show consistent success at producing valid plans but wide variation in handling the harder layers of conditional boldness, history-sensitive judgment, and role integration. This matters because real CEO decisions require exactly this synthesis under information asymmetry and organizational constraints rather than isolated reasoning tasks. The work identifies specific failure modes such as single-advisor capture and a consistent tradeoff where deeper integration correlates with less decisive action.

Core claim

In the CEO-Bench multi-round simulation, LLM agents must redirect capital across business units while receiving private signals from CFO, CTO, COO, and CMO advisors with distinct priorities; all tested models produce structurally valid plans yet show marked differences in strategic calibration, with systematic patterns of advisor capture, conservative defaults under ambiguity, historical amnesia, and an integration-boldness tradeoff.

What carries the argument

CEO-Bench, a multi-agent environment in which an LLM CEO agent receives role-conditioned private signals from four C-suite advisors and outputs multi-round allocation plans scored on role integration, conditional boldness, history-sensitive judgment, and plan validity.

If this is right

  • Models that engage more deeply with conflicting advisor perspectives produce less decisive allocation plans.
  • All tested models exhibit single-advisor capture and historical amnesia as recurring failure modes.
  • Current frontier LLMs remain limited in handling the full stack of executive capabilities required for organizational decision-making.
  • The benchmark results delineate a capability boundary that future AI-assisted executive systems must address.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of AI executive assistants may need explicit mechanisms to resolve the observed integration-boldness tradeoff.
  • Extending the benchmark to include measurable real-company financial outcomes could test whether calibration scores predict downstream performance.
  • The same multi-advisor structure could be adapted to evaluate LLMs in other domains with information asymmetry such as regulatory compliance or clinical resource allocation.

Load-bearing premise

The 13 scenarios and four role-conditioned advisors with private signals accurately capture the defining challenges of real executive decision-making under information asymmetry, organizational constraints, and temporal dependencies.

What would settle it

Running the same models on a new set of 13 scenarios drawn from documented historical corporate reallocation cases and checking whether models with higher strategic calibration scores produce allocations closer to the observed real-world outcomes.

Figures

Figures reproduced from arXiv: 2606.17459 by Lingfei Qian, Xueqing Peng, Yuyang Dai, Zhuohan Xie.

Figure 1
Figure 1. Figure 1: Summary of CEO-BENCH results across five models. (a) Overall scores. (b) Scores decomposed by evaluation dimension: role integration, boldness calibration, history sensitivity, and plan validity. (c) Average scores by difficulty tier (easy, fragile, adversarial). (d) Distribution of outcome grades per model: invalid plan, misallocated, acceptable with trade-offs, and high quality. Nemotron-Nano-9B (62) and… view at source ↗
read the original 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 that evaluates LLMs on CEO-level strategic resource reallocation -- the process of redirecting capital across business units in a multi-round, constraint-rich organizational environment. In \textsc{CEO-Bench}, LLM agents receive conflicting advice from four role-conditioned C-suite advisors (CFO, CTO, COO, CMO), each with private signals and distinct priorities, and must synthesize these into a concrete allocation plan evaluated along four dimensions: role integration, conditional boldness, history-sensitive judgment, and plan validity. Experiments across five frontier models on 13 scenarios reveal that all models achieve high structural validity but diverge sharply on strategic calibration -- the hardest capability layer. We identify systematic failure modes including single-advisor capture, conservative default under ambiguity, and historical amnesia, and uncover a structural integration-boldness tradeoff: models that engage more deeply with conflicting perspectives tend to produce less decisive action. These findings delineate the current capability boundary of LLMs as organizational decision-makers and inform the design of future AI-assisted executive systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper introduces CEO-Bench, a multi-agent simulation benchmark for evaluating LLMs on CEO-level strategic resource reallocation. In the benchmark, LLM agents must synthesize conflicting private signals from four role-conditioned C-suite advisors (CFO, CTO, COO, CMO) across 13 scenarios in a multi-round, constraint-rich setting. The evaluation uses four dimensions (role integration, conditional boldness, history-sensitive judgment, plan validity) and reports that five frontier models achieve high structural validity but diverge sharply on strategic calibration, with identified failure modes including single-advisor capture, conservative defaults, historical amnesia, and an integration-boldness tradeoff.

Significance. If the benchmark design and metrics hold, the work supplies a reproducible, multi-stakeholder evaluation framework that moves beyond isolated reasoning or economic tasks toward organizational decision-making under asymmetry and constraints. The explicit identification of systematic failure modes and the reported tradeoff between deeper integration and decisiveness would be a concrete contribution to delineating current LLM limits as executive agents and guiding future AI-assisted systems.

major comments (1)
  1. [Scenario Design and Evaluation Rubrics] The central claim that the observed divergence on strategic calibration delineates the capability boundary of LLMs as organizational decision-makers rests on the assumption that the 13 scenarios and four role-conditioned advisors with private signals accurately instantiate the defining challenges of real executive decisions. No validation against actual corporate cases, expert review of scenario realism, or sensitivity analysis to alternative advisor signal structures is reported; without this, the generalizability of the structural-validity vs. strategic-calibration split cannot be assessed.
minor comments (1)
  1. [Evaluation Metrics] The four evaluation dimensions are introduced in the abstract but their precise operationalization (e.g., how 'history-sensitive judgment' is scored from multi-round traces) should be stated with explicit rubrics or pseudocode in the main text for reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the importance of validating scenario design against real-world executive challenges. This feedback helps clarify the scope and limitations of CEO-Bench. Below we respond directly to the major comment and describe targeted revisions.

read point-by-point responses

  1. Referee: The central claim that the observed divergence on strategic calibration delineates the capability boundary of LLMs as organizational decision-makers rests on the assumption that the 13 scenarios and four role-conditioned advisors with private signals accurately instantiate the defining challenges of real executive decisions. No validation against actual corporate cases, expert review of scenario realism, or sensitivity analysis to alternative advisor signal structures is reported; without this, the generalizability of the structural-validity vs. strategic-calibration split cannot be assessed.

    Authors: We agree that the absence of direct validation against real corporate cases or formal expert review limits strong claims about external validity. The 13 scenarios were constructed to isolate core executive challenges—information asymmetry, conflicting stakeholder priorities, multi-round temporal dependencies, and hard resource constraints—drawing on established strategic management literature (e.g., capital reallocation under uncertainty and cross-functional conflict). Each scenario varies these dimensions systematically while keeping advisor roles fixed to standard C-suite mandates. This controlled, reproducible design enables the clean identification of failure modes and the integration-boldness tradeoff that would be difficult to isolate in observational corporate data. Nevertheless, we recognize that without external anchoring the generalizability of the structural-validity versus strategic-calibration distinction remains provisional. In the revised manuscript we will: (1) expand the Scenario Design subsection with an explicit mapping of each scenario to documented real-world analogs, (2) add a dedicated Limitations paragraph on external validity, and (3) include a sensitivity analysis that perturbs advisor signal strengths and conflict levels in two representative scenarios. These additions will better support the reported divergence while transparently bounding the claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; benchmark design and results are independently specified

full rationale

The paper introduces CEO-Bench as an explicit multi-agent simulation with fixed scenarios, advisor roles, private signals, and four evaluation dimensions (role integration, conditional boldness, history-sensitive judgment, plan validity). These elements are defined in the manuscript prior to any model runs. The reported outcomes—high structural validity across models with divergence on strategic calibration—are direct empirical measurements obtained by executing the five LLMs on the 13 scenarios. No parameter is fitted to a data subset and then relabeled as a prediction; no result is derived by construction from the inputs; no load-bearing claim rests on a self-citation chain; and no uniqueness theorem or ansatz is smuggled in. The mapping from observed outputs to capability layers can be inspected against the stated rubrics without reduction to the benchmark definition itself. This is a standard, self-contained empirical evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract; the contribution is the introduction of an evaluation benchmark rather than a theoretical derivation.

pith-pipeline@v0.9.1-grok · 5791 in / 1063 out tokens · 35214 ms · 2026-06-27T01:40:22.591334+00:00 · methodology

discussion (0)

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Reference graph

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