arXiv:2603. 03970v2 Announce Type: replace Abstract: Generative artificial intelligence (GenAI) is increasingly being integrated into complex business workflows, fundamentally shifting the boundaries of managerial decision-making.
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.
We show that ordinary business language --- "maximize profitability" --- induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambiguous signals of potential safety violations to s...
arXiv:2609.07731v1 Announce Type: new Abstract: We show that ordinary business language --- "maximize profitability" --- induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambi...
arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.
arXiv:2607. 26220v1 Announce Type: cross Abstract: Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees.
arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input...
Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model.
arXiv:2603. 14805v2 Announce Type: replace Abstract: Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation.
The paper titled "The Moral Check: Strategic AI Governance for the Pacing Problem" argues that technology cannot self‑steer and that strategy must guide AI development by ensuring purpose and judgment precede compute. It presents a dual contribution: a PRISMA 2020 review of 130 empirical studies and the Strategic AI Governance Ex‑Ante Framework (SAGE‑X), which operationalizes four strategic mindset pillars to mitigate velocity myopia, moral hazard, empirical hazard endpoints, and guardrail decay. The framework includes a calculable Moral Check Index and an Enterprise Lifecycle Audit Instrument to enforce that AI scaling does not outpace deliberative moral judgment, human agency, and societal trust.
arXiv:2606. 15575v1 Announce Type: new Abstract: Organizational knowledge is fragmented across a variety of software systems, tacit expertise, and manual documents that have traditionally been designed for human consumption.
arXiv:2608. 10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSecOps built for deterministic automation, across every scale of agency.
arXiv:2609.21841v1 Announce Type: new Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically val...