This paper evaluates the feasibility and cost-effectiveness of large-scale AI data centers in low-Earth orbit (LEO) versus terrestrial facilities, considering factors like launch cost, power generatio…

The paper introduces Global PSRO, a novel deep reinforcement learning framework that efficiently approximates Nash equilibria in large two-player zero-sum games by intelligently expanding the strategy…

The paper surveys the use of LLMs for agentic NetOps and AIOps, arguing that operational reliability depends not on the model itself, but on robust surrounding machinery and workflow-centered evaluati…

The paper proposes Operational AI Deployment Assurance (OADA), a governance framework that translates complex AI evaluation metrics and operational uncertainties into actionable, deployment-oriented a…

The paper proposes that party autonomy can be used to determine the applicable law for non-contractual obligations arising from cross-border data transfers by aligning it with the law chosen for the r…

This survey reviews how Large and Multi-modal Language Models (LLMs/MM-LLMs) are being applied to integrate diverse data sources for enhanced decision support in transportation systems management and…

The paper introduces DBA-Bench, a benchmark for evaluating database agents with production fidelity, outcome-first evaluation, and controlled scenario reproducibility.

The ORBIT project developed an agentic AI system, ORBIT, to address operational pain points in the Network Operations Center (NOC) workflow by integrating it into ServiceNow and providing routine auto…

The paper reframes manufacturing ransomware recovery from a simple backup restoration task to a complex critical-infrastructure continuity problem, proposing Minimum Viable Factory Recovery (MVF Recov…

The paper introduces OR-Space, a novel full-lifecycle workspace benchmark designed to rigorously evaluate industrial optimization agents by simulating real-world, multi-stage OR workflows that go beyo…

The paper details significant enhancements to the SONARR system's core logic, replacing restrictive Boolean logic with generic data type support and adding multi-compute capabilities to improve vulner…

This paper proposes an organizational memory for LLM-based agents to access and share enterprise-specific procedural knowledge for reliable business process execution.

The paper proposes a novel hybrid authorization framework that combines roles and First-Order Logic to enforce fine-grained, triple-level access control for autonomous agents interacting with knowledg…

This study investigated whether Security Operations Center (SOC) analysts can justify their decisions when triaging alarms, finding that while they are often correct in identifying true threats, they…

The paper introduces Security Level 5 (SL5), a new, highly stringent security standard for AI systems designed to withstand attacks from state-level, top-tier cyber adversaries.